Seminar 16. December

Philosophical and aesthetical perspectives

–report from meeting 16/12 Trondheim/Skype

Andreas Bergsland, Trond Engum, Tone Åse, Simon Emmerson, Øyvind Brandtsegg, Mats Claesson

The performers experiences of control:

In the last session (Trondheim December session) Tone and Carl Haakon (CH) worked with rhythmic regularity and irregularity as parameters in the analysis. They worked with the same kind of analysis, and the same kind of mapping analysis to effect parameter. After first trying the opposite, they ended up with: Regularity= less effect, Irregularity= more. They also included a sample/hold/freeze effect in one of the exercises. Øyvind commented on how Tone in the video stated that she thought it would be hard to play with so little control, but that she experienced that they worked intuitively with this parameter, which he found was an interesting contradiction. Tone also expressed in the video that on the one side she would sometimes hope for some specific musical choices from CH (“I hope he understands”) but on the other hand that she “enjoyed the surprises”. These observations became a springboard for a conversation about a core issue in the project: the relationship between control and surprise, or between controlling and being controlled. We try to point here to the degree of specific and conscious intentional control, as opposed to “what just happens” due to technological, systemic, or accidental reasons.  The experience from the Trondheim December session was that the musicians preferred what they experienced as an intuitive connection between input and outcome, and that this facilitated the process in the way that they could “act musically”. (This “intuitive connection” is easily related to Simon’s comment about “making ecological sense” later in this discussion.)  Mats commented that in the first Oslo session the performers stated that they felt a similarity to playing with an acoustic instrument. He wondered if this experience had to do with the musicians’ involvement in the system setup, while Trond pointed out that the Trondheim December session and Oslo session were pretty similar in this respect. A further discussion about what “control”, “alienation” and “intuitive playing” can mean in these situations seems appropriate.

Aesthetic and individual variables

This led to a further discussion about how we should be aware that the need for generalising and categorising – which is necessary at some point to actually be able to discuss matters – can lead us to overlook important variable parameters such as:

  • Each performer’s background, skills, working methods, aesthetics and preferences
  • That styles and genres relate differently to this interplay

A good example of this is Maja’s statement in the Brak/Rug session that she preferred the surprising, disturbing effects, which gave her new energy and ideas. Tone noted that this is very easy to understand when you have heard Maja’s music, and even easier if you know her as an artist and person. And it can be looked upon as a contrast to Tone/CH who seek a more “natural” connection between action and sounding result, in principle they want the technology to enhance what they are already doing. But, as cited above, Tone commented that this is not the whole truth. Surprises are also welcome in the Tone/Carl Haakon collaboration.

Simon underlined, because of these variables, the need to pin down in each session what actually happens, and not necessarily set up dialectical pairs. Øyvind pointed out, on the other hand, the need to lay out possible extremes and oppositions to create some dimensions (and terms) along which language can be used to reflect on the matters at hand.

Analysing or experiencing?

Another individual variable, both as audience and performer, is the need to analyse, to understand what is happening in the perceiving of a performance. One example brought up related to this was Andreas’ experience of his change of audience perspective after he studied ear training. This new knowledge led him to take an analysing perspective, wanting to know what happened in a composition when performed. He also said: “as an audience you want to know things, you analyse”. Simon referred to “the inner game of tennis” as another example: how it is possible to stop appreciating playing tennis because you become too occupied analysing the game – thinking of the previous shot rather than clearing the mind ready for the next. Tone pointed at the individual differences between performers, even within the same genre (like harmonic jazz improvisation) – some are very analytic, also in the moment of performing, while others are not. This also goes for the various groups of audiences, some are analytic, some are not – and there is also most likely a continuum between the analytic and the intuitive musician/audience.  Øyvind mentioned experiences from presenting the crossadaptive project to several audiences over the last few months. One of the issues he would usually present is that it can be hard for the audience to follow the crossadaptive transformations, since it is an unfamiliar mode of musical (ex)change.  However, responses to some of the simpler examples he then played (e.g. amplitude controlling reverb size and delay feedback), yielded the response that it was not hard to follow. One of the places where this happened was Santa Barbara, where Curtis Roads commented he thought it quite simple and straightforward to follow. Then again, in the following discussion, Roads also conceded that it was simple because the mapping between analysis parameter and modulated effect was known. Most likely it would be much harder to deduce what the connection was just by listening alone, since the connection (mapping) can be anything. Cross Adaptive processing may be a complicated situation, not easy to analyse either for the audience or the performer. Øyvind pointed towards differences in parameters, as we had also discussed collectively: That some were more “natural” than others, like the balance between amplitude and effect, while some are more abstract, like the balance between noise and tone, or regular/irregular rhythms.

Making ecological sense/playing with expectations

Simon pointed out that we have a long history of connections to sound: some connections are musically intuitive because we have used them perhaps for thousands of years, they make ‘ecological’ sense to us. He referred to Eric Clarke’s “ Ways of listening: An ecological approach to the perception of musical meaning (2005)” and William Gaver “What in the world do we hear?: An ecological approach to auditory event perception” (1993). We come with expectations towards the world, and one way of making art is playing with those expectations. In modernist thinking there is a tendency to think of all musical parameters as equal – or at least equally organised – which may easily undermine their “ecological validity” – although that need not stop the creation of ‘good music’ in creative hands.

Complexity and connections

So, if the need for conscious analysis and understanding will vary between musicians, is this the same for the experienced connection between input and output? And what about the difference between playing and listening as part of the process, or just listening, either as a musician, musicologist, or an audience member? For Tone and Carl Haakon it seemed like a shared experience that playing with regularity/non- regularity felt intuitive for both – while this was actually hard for Øyvind to believe, because he knew the current weakness in how he had implemented the analysis. Parts of the rhythmic analysis methods implemented are very noisy, meaning they produce results that sometimes can have significant (even huge) errors in relation to a human interpretation of the rhythms being analysed. The fact that the musicians still experienced the analyses as responding intuitively is interesting, and it could be connected to something Mats said later on: “the musicians listen in another way, because they have a direct contact with what is really happening”. So, perhaps, while Tone &CH experienced that some things really made musical sense, Øyvind focused on what didn’t work – which would be easier for him to hear?  So how do we understand this, and how is the analysis connected to the sounding result? Andreas pointed out that there is a difference between hearing and analysing: you can learn how the sound behaves and work with that. It might still be difficult to predict exactly what will happen.Tone’s comment here was that you can relate to a certain unpredictability and still have a sort of control over some larger “groups of possible sound results“ that you can relate to as a musician. There is not only an urge to “make sense” (= to understand and “know” the connection) but also an urge to make “aesthetical sense”.

With regards to the experienced complexity, Øyvind also commented that the analysis of a real musical signal is in many ways a gross simplification, and by trying to make sense of the simplification we might actually experience it as more complex. The natural multidimensionality of the experienced sound is lost, due to the singular focus on one extracted feature. We are reinterpreting the sound as something simpler. An example mentioned was the vibrato, which is a complex input and a complex analysis, that could in some analyses be reduced to a simple “more or less” dimension. This issue also relates to the needs of our project to construct new methods of analysis, so that we can try to find analysis dimensions that correspond to some perceptual or experiential features.

Andreas commented “It is quite difficult to really know what is going on without having knowledge of the system and the processes. Even simple mappings can be difficult to grasp only by ear”. Trond reminded us after the meeting about the further complexity that was perhaps not so present in our discussion: we do not improvise with only one parameter “out of control” (adaptive processing). In the cross adaptive situation someone else is processing our own instrument, so we do not have full control over this output, and at the same time we do not have any control over what we are processing, the input (cross- adapting), which in both cases could represent an alienation and perhaps a disconnection from the input-result- relation. And of course the experience of control is also connected to “understanding” the processing analysis you are working with.

The process of interplay:

Øyvind referred to Tone’s experience of a musical “need” during the Trondheim session, expressed: ”I hope he understands…” –  when she talked about the processes in the interplay. This was pointing at how you realise during the interplay that you have very clear musical expectations and wishes towards the other performer.  This is not in principle different from a lot of other musical improvising situations. Still, because you are dependent on the other’s response in a way that is defining not only the wholeness, but your own part in it, this thought seemed to be more present than is usual in this type of interplay.

Tools and setup

Mats commented that very many of the effects that are used are about room size, and that he felt that this had some – to him – unwanted aesthetical consequences. Øyvind responded that he wanted to start with effects that are easy to control and easy to hear the control of. Delay feedback and reverb size are such effects. Mats also suggested that it was an important aesthetical choice not to have effects all the time, and thereby have the possibility to hear the instrument itself. So to what extent should you be able to choose? We discussed the practical possibilities here: some of the musicians (for example Bjørnar Habbestad) have suggested a foot pedal,  where the musician could control the degree to which their actions will inflict changes on the other musician’s sound (or the other way around, control the degree to which other forces can affect his sound). Trond suggested one could also have control over the level of signal/output  for the effects, adjusting the balance between processed and unprocessed sound. As Øyvind commented, these types of control could be a pedagogical tool for rehearsing with the effect, turning the processing on and off to understand the mapping better. The tools are of course partly defining the musician’s balance between control, predictability and alienation. Connected to this, we had a short discussion regarding amplified sound in general, that the instrumental sound coming from a speaker located elsewhere in the room in itself could already represent an alienation. Simon referred to the Lawrence Casserley /Evan Parker principle of “each performer’s own processor”, and the situation before the age of the big PA, where the electronic sound could be localised to each musician’s individual output. We discussed possibilities and difficulties with this in a cross adaptive setting: which signal should come out of your speaker? The processed sound of the other, or the result of the other processing you? Or both? and then what would the function be – the placement of the sound is already disturbed.

Rhythm

New in this session was the use of the rhythmical analysis. This is very different from all other parameters we have implemented so far. Other analyses relate to the immediate sonic character, but rhythmic analysis tries to extract some  temporal features, patterns and behaviours. Since much of the music played in this project is not based on a steady pulse, and even less confined to a regular grid (meter), the traditional methods of rhythmic analysis will not be appropriate. Traditionally one will find the basic pulse, then deduce some form of meter based on the activity, and after this is done one can relate further activity to this pulse and meter. In our rhythmical analysis methods we have tried to avoid the need to first determine pulse and meter, but rather looked into the immediate time relationships between neighbouring events. This creates much less support for any hypothesis the analyser might have about the rhythmical activity, but also allows much greater freedom of variation (stylistically, musically) in the input. Øyvind is really not satisfied with the current status of the rhythmic analysis (even if he is the one mainly responsible for the design), but he was eager to hear how it worked when used by Tone and Carl Haakon.  It seems that the live use by real musicians allowed the weaknesses of the analyses to be somewhat covered up. The musicians reported that they felt the system responded quite well (and predictably) to their playing. This indicates that, even if refinements are much needed, the current approach is probably a useful one. One thing that we can say for sure is that some sort of rhythmical analysis is an interesting area of further exploration, and that it can encode some perceptual and experiential features of the musical signal in ways that make sense to the performers. And if it makes sense to the performers, we might guess that it will have the possibility of making sense to the listener as well.

Andreas: How do you define regularity (ex. claves-based musics), how “less regular” is that from a steady beat.

Simon: If you ask a difficult question with a range of possible answers this will be difficult to implement within the project.

As a follow up to the refinement of rhythmic analysis, Øyvind asked:  how would *you* analyze rhythm?

Simon: I wouldn’t analyze rhythm for example timeline in African music: a guiding pulse that is not necessarily performed and may exist only in the performer’s head. (This relates directly to Andreas’s next point – Simon later withdrew the idea that he would not analyse rhythm and acknowledged its usefulness in performance practice.)

Andreas: Rhythm is a very complex phenomenon, which involves multiple interconnected temporal levels, often hierarchically organised. Perceptually, we have many ongoing processes involving present, past and anticipations about future events. It might be difficult to emulate such processes in software analysis. Perhaps pattern recognition algorithms can be good for analysing rhythmical features?

Mats: what is rhythm? In our examples: gesture may be more useful than rhythm

Øyvind: rhythm is repeatability, perhaps? Maybe we interpret this in the second after

Simon: no I think we interpret virtually at the same time

Tone: I think of it as a bodily experience first and foremost.  (Thinking about this in retrospect, Tone adds: The impulses -when they are experienced as related to each other- creates a movement in the body. I register that through the years (working with non-metric rhythms in free improvisation) there is less need for a periodical set of impulses to start this movement. (But when I look at babies and toddlers, I recognise this bodily reaction to irregular impulses.) I recognice what Andreas says- the movement is trigged by the expectation of more to come. (Think about the difference in your body when you wait for the next impulse to come (anticipations) and when you know that it is over….)

Andreas: When you listen to rhythms, you group events on different levels and relate to what was already played. The grouping is often referred to as «chunking» (psychology). Thus, it works both on an immediate level (now) as well as a more overarching level (bar, subsection, section) because we have to relate what we hear to the earlier. You can simplify or make it complex

Oslo, First Session, October 18, 2016

First Oslo Session. Documentation of process
18.11.2016

Participants
Gyrid Kaldestad, vocal
Bernt Isak Wærstad, guitar
Bjørnar Habbestad, flute

Observer and Video
Mats Claesson

The Session took place in one of the sound studios at the Norwegian Academy of Music, Oslo , Norway

Gyrid Kaldestad (vocal) and Bernt Isak Wærstad (guitar) had one technical/setup meeting beforehand, and there were numerous emails going back and forth before the session that was about technical issues.
Bjørnar Habbestad (flute) where invited into the session.

The observer, decided to make a video documentation of the session.
I’m glad I did because I think it gives a good insight off the process. And a process it was!
The whole session lasted almost 8 hours and it was not until the very last 30 minutes that playing started.

I am (Mats Claesson) not going to comment on the performative musical side of the session. The only reason for this is that the music making happend at the very end of the session, was very short and it was not recorded so I could evaluate it “in depth” However, just watch the comments, from the participants, at the end of the video. They are very positive…..
I think from the musicians side it was rewarding and highly interesting. I am confident that the next session will generate an musical outcome that is substantial enough to be comment on, from both a performative and a musical side.

In the video there are no processed sound of the very last playing due to use of headphone, but you can listen to excerpts posted below the video.

Here is a link to the video

Reflections on the process given from the perspective of the musicians:

We agreed to make a limited setup to have better control over the processing. Starting with basic sounds and basic processing tools so that we easier could control the system in a musical way. We started with a tuning analysis for each instrument (voice, flute, guitar)

Instead of chosing analysis parameter up front, we analysed different playing techniques, e.g. non- tonal sounds (sss, shhh), multiphonics etc., and saw how the analyser responded. We also recorded short samples of the different techniques that each of us usually play, so that we could investigate the analysis several times.  

This is the analysis results we got:

analysis

Since we’re all musicians experienced with live processing, we made a setup based on effects that we already know well and use in our live-electronic setup (reverb, filter, compression, ring modulation and distortion).

To set up meaningful mappings, we chose an approach that we entitled “spectral ducking”, where a certain musical feature on one instrument would reduce the same musical feature on the other – e.g. a sustained tonal sound produced by the vocalist, would reduce harmonic musical features of the flute by applying ring modulation. Here is a complete list of the mappings used:

mapping

Excerpt #1 – Vocal and flute

Excerpt #2 – Vocal and flute

Excerpt #3 – Vocal and flute

Excerpt #4 – Vocal and flute

Lack of consisive and presise analysis results from the guitar in combination with time limitation, it wasn’t possible to set up mappings for the guitar and flute. We did however test out the guitar and flute in the last minutes of the session, where the guitar simply took the role of the vocal in terms of processing and mapping. A knowledge of the vocal analysis and mapping, made it possible to perform with the same setup even though the input instrument had changed. Some short excerpts from this performance can be heard below.

Excerpt #5 – Guitar and flute

Excerpt #6 – Guitar and flute

Excerpt #7 – Guitar and flute

 Reflections and comments:

  • We experienced the importance of exploring new tools like this on a known system. Since none of us knew Reaper from before, we used spent quite a lot of time learning a new system (both while preparing and during the session)
  • Could the meters analyser be turned the other way around? It is a bit difficult to read sideways.
  • It would be nice to be able to record and export control data from the analyser tool that will make it possible to use it later in a synthesis.
  • Could it be an idea to have more analyzer sources pr channel? The Keith McMillian Softstep mapping software could possibly be something to look at for inspiration?
  • The output is surprisingly musical – maybe this is a result of all the discussions and reflections we did before we did the setup and before we played?
  • The outcome is something else than playing with live electronics- it is immediate and you can actually focus on the listening – very liberating from a live electronics point of view!
  • The system is merging the different sounds in a very elegant way.
  • Knowing that you have an influence on your fellow musicians output forces you to think in new ways when working with live electronics.
  • The experience for us is that this is similar to work acoustically.

 

Seminar 21. October

We were a combination of physically present and online contributors to the seminar.  Joshua Reiss and Victor Lazzarini participated via online connection, present together in Trondheim were: Maja S.K. Ratkje, Siv Øyunn Kjenstad, Andreas Bergsand, Trond Engum, Sigurd Saue and Øyvind Brandtsegg

Instrumental extension, monitoring, bleed

We started the seminar by hearing from the musicians how it felt to perform during Wednesday’s session. Generally, Siv and Maja expressed that the analysis and modulation felt like an extension to their original instrument. There were issues raised about the listening conditions, how it can be difficult to balance the treatments with the acoustic sound. One additional issue in this respect when working cross-adaptively (as compared to e.g. the live processing setting), is that there is not a musician controlling the processing, to the processed sound is perhaps a little bit more “wild”. In a live processing setting, the musician controlling the processing will attempt to ensure a musically shaped phrasing and control that is at the current stage not present in the crossadaptive situation. Maja also reported acoustic bleed from the headphones to the feature extraction for her sound. With this kind of sensitivity to crosstalk, the need for source separation (as discussed earlier) is again put to our attention.  Adjusting the signal gating (noise floor threshold for the analyzer) is not sufficient in many situations, and raising the threshold also lowers the analyzer sensitivity to soft playing. Some analysis methods are more vulnerable than others, but it is safe to say that none of the analysis methods are really robust against noise or bleed from external signals.

Interaction scenarios as “assignments” for the performers

We discussed the different types of mapping (of features to modulators) which the musicians also called “assignments”, as it was experienced as a given task to perform utilizing certain expressive dimensions in specific ways to control the timbre of the other instrument. This is of course true. Maja expressed that she was most intrigued by the mappings that felt “illogical”, and that illogical was good. By illogical, she means mappings that does not feel natural or as intuitive musical energy flows. Things that break up the regular musical attention, and break up the regular flow from  idea to expression. As an example was mentioned the use of pitch to control reverberation size. For Maja (for many, perhaps for most musicians), pitch is such a prominent parameter in the shaping of a musical statement, so it is hard to play when some external process interferes with the intentional use of pitch. The linking of pitch to some timbral control is such an interference, because it creates potential conflict between the musically intended pitch trajectory and the musically intended timbral control trajectory. An interaction scenario (or in effect, a mapping from features to modulators to effects), can in some respects be viewed as a composition, in that it sets a direction for the performance. In many ways similar to the text scores of the experimental music of the 60’s, where the actual actions or events unfolding are perhaps not described, but more a description of an idea of how the musicians may approach the piece. For some, this may be termed a composition, others might use the term score. In any case it dictates or suggests some aspects of what the performers might do, and as such consists of an external  implication on the performance.

Analysis, feature extraction

Some of our analysis methods are still a bit flaky, i.e. we see spurious outliers in their output that is not necessarily caused by perceptible changes in the signal being analyzed. One example of this is the rhythm consonance feature, where we try to extract a measure of rhythmic complexity by measuring neighboring delta times between events and looking for simple ratios between these. The general idea being that simpler the ratio, the simpler the rhythm is. The errors sneak in as part of the tolerance for human deviation in rhythmic performance, where one may clearly identify one intended pattern, while the actual measured delta times can deviate more than a musical time division (for example, when playing a jazzy “swing 8ths” triplet pattern, which may be performed somewhere between equal 8th notes, a triplet pattern, or even towards a dotted 8th plus a 16th and in special cases a double dotted 8th plus a 32nd). When looking for simple integer relationships small deviations in phrasing may lead to large changes in the algorithm’s output. For example 1:1 for a straight repeating 8th pattern, 2:1 for a triplet pattern and 3:1 for a dotted 8th plus 16th pattern, re the jazz swing 8ths. See also this earlier post for more details if needed.  As an extreme example, think of a whole note at a slow tempo (1:1) versus an accent on the last 16th note on a measure (giving a 15:16 ratio). These deviations create single values with a high error. Some common ways of dampening the effect of such errors would be lowpass filtering, exponential moving average, or median filtering. One problem in the case of rhythmic analysis is that we only get new values for each new event, so the “sampling rate” is variable and also very low. This means that any filtering has the consequence of making the feature extraction respond very slowly (and also with a latency that varies with the number of events played), something that we would ideally try to avoid.

Stabilizing extraction methods

In the light of the above, we discussed possible methods for stabilizing the feature extraction methods to avoid the spurious large errors. One conceptual difficulty is differentiating between a mistake and an intended expressive deviation.  More importantly, to differentiate between different possible intended phrasings. How do we know what the intended phrasing is without embedding too many assumptions in our analysis algorithm? For rhythm, it seems we could do much better if we first deduct the pulse and the meter, but we need to determine what our (performed) phrasings are to be sure of the pulse and meter, so it turns into a chicken and egg problem. Some pulse and meter detection algorithms maintain several potential alternatives, giving each alternative a score for how well it fits in light of the observed data. This is a good approach, assuming we want to find the pulse and meter. Much of the music we will deal with in this project does not have a strong regular pulse, and it most surely does not have a stable meter. Let’s put the nitty gritty details of this aside for a moment, and just assume that we need some kind of stabilizing mechanism. As Josh put it, a restricted form of the algorithm.
Disregarding the actual technical difficulties, let’s say we want some method of learning what the musician might be up to, what is intended, what is mistake, and what are expressive deviations from the norm. Some  sort of calibration the normal, average, or regularly occurring behavior. Track change during session, and this is change relative to the norm as established in the traning. Now, assuming we could actually build such an algorithm, when should it be calibrated (put into learn mode)? Should we continuously update the norm, or just train once and for all? If training before performance (as some sort of sound check), we might fail miserably because the musician might do wildly different things in the “real” musical situation compared to when “just testing”. Also, if we continuously update the norm, then our measures are always drifting, so something that was measured as “soft” in the beginning of the performance might be indicated as something else entirely by the end of the performance. Even though we listen to musical form as a relative change, we might also as listeners recognize when “the same” happens again later. E.g. activity in the same pitch register, the same kind of rhythmic density, the same kind of staccato abrupt complex rhythms etc. with a continuously updated norm, we might classify the same as something different. Regarding the attempt to define something as the same see also the earlier post on philosophical implications. Still, with all these reservations, it seems necessary to attempt creating methods for relative change. This can perhaps be used as a restricted form, as Joshua suggests, or in any case as an interesting variation on the extracted features we already have.  It would extend the feature output formats of absolute value, dynamic range, crest. In some ways it is related to dynamic range (f.ex. as the relative change would to some degree be high when the dynamic range is high, but then again the relative change would have a more slowly moving reference, and it would also be able to go negative). As a reference for the relative change, we could use a long time average, a model of expectation, assumption of the current estimate (maintaining several candidates as with pulse and meter induction), or normal distribution and standard deviation. These long term estimates have been used with success in A-DAFx (adaptive audio effects) for live applications.

Josh mentioned the possibility of doing A/B comparision of the extraction methods with some tests at QMUL. We’ll discuss this further.

Display of extracted features

When learning (an instrument), multimodal feedback can significantly reduce the time taken to gain proficiency. When learning how the different feature extraction methods work, and how they react to intentional expressive changes in the signal, visual feedback might be useful. Other means of feedback could be sonifying the analysis (which is what we do already, but perhaps make it more pointed and simple. This could be especially useful when working with the gate mix method, as the gate will give not indication to the performer that it will soon open, whereas a sonification of the signal could aid the performer in learning the range of the feature and getting an intuitive feel for when the gate will open. Yet another means of feedback is giving the performer the ability to adjust the scaling of the feature-to-modulator mapping. In this way, it would act somewhat like giving the performer that ability to tune the instrument, ensuring that it reacts dynamically to the ranges of expression utilized by this performer. Though not strictly a feedback technique, we could still treat it as a kind of familiarization aid in that it acts as a two-way process between performer and instrument. The visualization and the performer adjustable controls could be implemented as a small GUI component running on portable devices like cellphone or touchpad. The signals can be communicated from the analyzer and MIDIator via OSC, and the selection of features to display can be controlled by the assignments of features in the MIDIator. A minimal set of controls can be exposed, and the effects of these being mirrored in the plugins (MIDIator). Some musicians may prefer not to have such a visual feedback. Siv voiced concern that she would not be so interested in focusing visually on the signals. This is a very valid concern for performance. Let us assume it will not be used during actual performance, but as a tool for familiarization during early stages of practice with the instrument.

Brief system overview and evaluation

As preparation for upcoming discussions about tecnical needs in the project, it seems appropriate to briefly describe the current status of the software developed so far.

analyzer_2016_10
The Analyzer

The plugins

The two main plugins developed is the Analyzer  and the MIDIator. The Analyzer extracts perceptual features from a live audio signal and transmit signals representing these features over a network protocol (OSC) to the MIDIator. The job of the MIDIator is to combine different analyzed features (scaling, shaping, mixing, gating) into a controller signal that we will ultimately use to control some effect parameter. The MIDIator can run on a different track in the same DAW, it can run on another DAW, or on another computer entirely.

Strong points

The feature extraction generally works reasonably well for the signals it has been tested on. Since a limited set of signals is readily available during implementation, some overfitting to these signals can be expected. Still, a large set of features is extracted, and these have been selected and tweaked for use as intentional musical controllers. This can sometimes differ from the more pure mathematical and analytical descriptions of a signal. The quality of of our feature extraction can best be measured in how well a musician can utilize it to intentionallly control the output. No quantitative mesurement of that sort have been done so far. The MIDIator contains a selection of methods to shape and filter the signals, and to combine them in different ways. Until recently, the only way to combine signals (features) was by adding them together. As of the past two weeks, mix methods for absolute difference, gating, and sample/hold has been added.

midiator_modules_2016_10
MIDIator modules

Weak points

The signal chain transmission from Analyzer to MIDIator, and then again from the MIDIator to the control signal destination each incurs at least one sample block latency. The size of a sample block can vary from system to system, but regardless of the size used our system will have 3 times this latency before an effect parameter value changes in response to a change in the audio input. For many types of parameter changes this is not critical, still it is a notable limitation of the system.

The signal transmission latency points at another general problem, interfacing between technologies. Each time we transfer signals from one paradigm to another we have the potential for degraded performance, less stability and/or added latency. In our system the interface from the DAW to our plugins will incur a sample block of latency, the interface between Csound and Python can sometimes incure performance penalties if large chunks of data needs to be transmitted from one to the other. Likewise, the communication between the Analyzer and MIDIator is such an interface.

Some (many) of the feature extraction methods create somewhat noisy signals. With noise, we mean here that the analyzer output can intermittently deviate from the value we perceptually assume to be “correct”. We can also look at this deviation statistically, if we feed it relatively (perceptually) consistent signals and look at how stable the output of each feature extraction method is. Many of the features show activity generally in the right register, and a statistical average of the output corresponds with general perceptual features. While the average values are good, we will oftentimes see spurious values with relatively high deviation from the general trend. From this, we can assume that the feature extraction model generally works, but intermittently fails. Sometimes, filtering is used as an inherent part of the analysis method, and in all cases, the MIDIator has a moving exponential average filter with separate rise and fall times. Filtering can be used to cover up the problem, but better analysis methods would give us more precise and faster response from the system.

Audio separation between instruments can sometimes be poor. In the studio, we can isolate each musician, but if we want them to be able to play together naturally in the same room, a significant bleed from one instrument to the other will occur. For live performance this situation is obviously even worse. The bleed give rise to two kinds of problems: Signal analysis is disturbed by the signal bleed, and signal processing is cluttered. For the analysis, it does not matter if we had perfect analysis methods if the signal to be analyzed is a messy combination of opposing perceptual dimensions. For the effect processing, controlling an effect parameter for one instrument leads to a change in the processing of the other instrument, just because the other instruments’ sound bleed into the first instrument’s microphones

Useful parameters (features extracted)

In many of the sessions up until now, the most used features has been amplitude (rms) and transient density. One reson for this is probably that they are concptually easy to understand, another is that their output is relatively stable and predictable in relation to the perceptual quality of the sound analyzed. Here are some suggestions of other parameters that expectedly can be utilized effectively in the current implementation:

  • envelope crest (env_crest): the peakyness of the amplitude envelope, for sustained sounds this will be low, for percussive onsets with silence between evens it will be high
  • envelope dynamic range (env_dyn): goes low for signals operating at a stable dynamic level, high for signals with a high degree of dynamic variation.
  • pitch: well known
  • spectral crest (s_crest): goes low for tonal sounds, medium for pressed tones, high for noisy sounds.
  • spectral flux (s_flux): goes high for noisy sounds, low for tonal sounds
  • mfccdiff: measure of tension or pressedness, described here

There is also another group of extracted features that is potentially useful but still has some stability issues

  • rhythmic consonance (rhythm_cons) and rhythmic irregularity (rhythm_irreg): described here
  • rhythm autocorr crest (ra_crest) and rhythm autocorr flux (ra_flux): described here

The rest of the extracted features can be considered more experimental, in some cases they might yield effective controllers, especially when combined with other features in reasonable proportions

Rhythm analysis, part 2

As mentioned in the rhythm analysis part 1, one of our goals at this point has been to try to find methods of rhythmical analysis that work without assumptions about pulse and meter, and also as far as possible without assumptions about musical style. As our somewhat minimal rhythm definition  we look at rhythm as time ratio constellations, patterns of time ratios. Here we will make an assumption (yes, something must be assumed)  regarding patterns of time durations: Recurring or repeated patterns have another perceptual quality than constantly shifting combinations of time durations. We could also assume that recurring or repeated patterns have stronger perceptual influence, but technically, it does not matter so much. The main issue is that we can measure some difference in quality. Quality here does not imply that something is better, just that something is different.

FFT of modified amplitude envelope

To measure how much recurrence there is in a signal, we can use autocorrelation. Repeated patterns will show up as peaks in the autocorrelation, and the period of repetition will be shown as the position of the peaks. Longer repetition periods give peaks further away (commonly further to the right when graphing the autocorrelation). To calculate the autocorrelation, we could use the FFT of the amplitude envelope as our basis. However, the unmodified envelope can have many variations at frequencies not related to the actual rhythms. For example, the amplitude envelope of a signal with fast transients (e.g. percussion, piano) show much more high frequency content than a signal with slow transients (e.g. many wind instruments). For this reason, we opted to use a modified envelope for the FFT in connection with the rhythm autocorrelation measure. The modified envelope is generated by using transient detection, triggering a short gaussian envelope scaled to the current amplitude of the signal. This way, we achieve a consistent envelope across different instruments, preserving the relative amplitude differences between transients (since we assume dynamics to be relevant to rhythm, for example in using accents to signify grouping of events).

Time spans and latency

One question to ask when analyzing for recurring patterns is “how long patterns are we looking for?”. One could say that a musical pattern sometimes will repeat relatively quickly, say once every second. Other times we can have arbitrary long patterns (sometimes very long), but for practical purposes, lets assume a maximum length of somewhere around 10 seconds. Now, if we want to analyze for such long patterns (10 seconds), an inherent limitation of any technique used would imply that it takes at least so many seconds to give an answer to whether there is a repeating pattern of that duration. Analyzing for 1-second patterns, we can have an indication after 1 second, and we can be sure after 2 seconds. For a musically responsive analysis, we’d like as low latency as possible, and in any practical case a latency of 10 seconds or more is not particularly responsive. Still, if the analysis window is shorter, we will not be able to detect longer patterns. One common method in FFT is to use overlapping windows, meaning that we update our analysis several times (with new data) within the time span defined by one analysis window. This will give us updated data more frequently, but still the longer patterns will only partially be influencing the output until a full period has passed. To alleviate this, we used 3 analysis durations running in parallel layers (each with overlapping windows as previously described). The longest time span is set to 10 seconds, layered onto this is a time span of 5 seconds, and layered on top of this again a 2.5 second time span. The layers are mixed down using a weighted sum that give precedence to more recent data while retaining the larger time span context. The shortest time span layer will be updated twice as often as the medium time span layer, and this again is updated twice as often as the longest time span layer. When we have a new frame of the longer time span, we will also have a new frame of the short time span ,these are then weighted 2/3 and 1/3 respectively. At the next available frame for the shorter time span, we weigh the new frame 2/3, and the longer frame 1/3. This is combined similarly for all the 3 layers to form the final autocorrelation coefficients. These are shown as a graph in the GUI.

rhythmcorrgraph1
3-layer rhythm autocorrelation. The red line is the shortest FFT, Light blue is the medium, Green is the longest. Yellow line is the weighted sum. This figures shows a steady rhythm played statically over the full duration of the longest FFT layer (10 seconds).
rhythmcorrgraph2
3 layer rhythm autocorrelation, as above. Here we see the recently played faster rhythms, with the long time span showing the static slower pulse. The combined correlation (yellow) has both features.

 

 

 

rhythmcorrgraph3
3 layer rhythm autocorrelation, as above. Showing a more randomly spaced non-repeating rhythm.
rhythmcorrgraph4
As above. Recently played steady fast rhythms after a period of randomly spaced non-repeating activity

Features under development

We have assumed that we could use the exact position of the peaks to detect the duration of repeated patterns. This is partly true, since fast rhythms (short repetition duration) give tightly spaced peaks, slow rhythms give sparsely spaced peaks. It seems so far, however, that the exact position and amplitude of each peak is not really stable enough to just use these values for that purpose. We use a peak picking algorithm to detect the highest peak, the second and third highest peak, and also the peak closest in time to the maximum peak and the first (earliest, leftmost) peak. In situations where the rhythm is quite static, these values correspond well to the repetition durations and the relative strength (in dynamics) of different repeating patterns. For most practical situations however, this specific application of the rhythmic autocorrelation does not really work that well (yet!). Another way of using these values could be to determine the degree to which the rhythmic events can be placed on a grid. We have termed this gridness. It is calculated by taking the maximum peak as the reference duration, then looking at the other detected peaks, and checking if we can find some relatively simple integer ratio between the max and each other peak. For example, if we have a max peak at 6 seconds, and then a second highest peak at 4 seconds, the ratio would be 3:2. In this case we construct a grid of 1/3 durations of the max peak across the whole time segment (1/3, 2/3, 3/3, 4/3, 5/3 … and so on as far as we can go), and see how many of all detected peaks correspond to grid locations. The process is repeated for each integer ratio found between the max peak and the lesser peaks. The grid with the maximum number of corresponding events wins, and is reported as the current rhythmic subdivision. The number of peaks that fall on this grid is divided by the number of detected events, resulting in the gridness value. If all detected events fall on grid locations, the gridness will be 1.0, if half of the events fall on the grid the gridness will be 0.5. The gridness measure does not currently work very well, due to the instability of the peaks as described above. This is an area of the analyzer where substantial refinement can take place. The resulting metrics (if working) however, seems intuitively to make sense; Measuring  periods of repetition, subdivisions of these, and the degree of consistence with the assumed grid of subdivisions.

So what can we actually use it for now?

Following up on a suggestion from Miller Puckette, we tried to look at more general features of the correlation graph, to extract some global descriptors of the current rhythmic activity. We turned to well known statistics like crest and flux .
The crest factor describing how “peaky” the signal is, that is the relation between the highest peak and the overall effective amplitude. Traditionally the crest would be calculated as the peak value divided by the rms (root-mean-square) amplitude. However, our use here is somewhat different than both the regular envelope crest and the spectral crest. For this specific purpose, we found it better to divide the rms value by the number of transients detected. This may at first seem counterintuitive, but can be explained by the fact that repeated patterns of the same duration creates many events that fall on the same peak locations in the autocorrelation, effectively making those peaks stronger. The division on the number of peaks detected avoids the exessively high crest values we could get if there was a single peak in the correlation. As such it gives a measure of the amount of repetition. It could be discussed whether we should use another term for this feature, to avoid confusion between this and the other creest measures.
The flux generally describes the amount of change from frame to frame, so static patterns will have low flux while constantly shifting rhythms will have high flux. The flux is calculated similarly as it would be for spectral flux (multiplying each value in a frame with the corrresponding value in the previous frame, accumulating the result and then normalizing it). This is done with a slight twist in our implementation; Because of the instability in the peaks’ location we don’t just multiply each value with the corresponding value of another frame, but we check if any neightbouring values have higher amplitude, and then use the maximum (this value or its neighbour on either side). We can think of this as the minimum possible flux. Empirical testing has shown it to be a more stable measure than the simpler variation of the flux measure.

rhythmautocorr_full
All results related to the rhythmic autocorrelation. The crest and flux are relatively reliable measures. The other ones subject to refinement. Note that the AC peaks in the lower half of the figure currently refers only to the long time span (green) autocorrelation.