Tag Archive Max

ByLukas Körfer

Speaking Objects

Abstract

In this project, an audio-only augmented reality sound installation was created as part of the course „Studienprojekte Musikprogrammierung“ (“Study Projects Music Programming”) at the Karlsruhe University of Music. It is important for the following text to distinguish the terminology from virtual reality (VR for short), in which the user is completely immersed in the virtual world. Augmented reality (AR for short) is the extension of reality through the technical addition of information.

 

Motivation

On the one hand, this sound installation should meet a certain artistic standard, on the other hand, my personal goal was to bring AR and especially auditory AR closer to the participants and to get them excited about this new technology. Unfortunately, augmented reality is very often only understood as the visual representation of information, as is the case with navigation systems or smartphone applications, for example. However, in my opinion, it is important to sensitize people more and more to the auditory extension of reality. I am convinced that this technology also has enormous potential and that there is a lot of catching up to do in terms of public awareness compared to visual augmented reality. There are already numerous areas of application in which the benefits of auditory AR have been demonstrated. These range from areas in which many applications of visual AR can already be found, such as education, increasing productivity or purely for entertainment purposes, to specialist areas such as medicine. Ten years ago, for example, there were already attempts to use auditory AR to enhance the sense of hearing for people with visual impairments. By sonifying real objects, it was possible to create a purely auditory orientation aid.

 

Methodology

In this project, participants should be able to move freely in a room in which objects are positioned and although these do not produce sounds in reality, the participants should be able to perceive sounds through headphones. In this sense, it is an extension of reality (“augmented reality”), as information is added to reality in auditory form using technical means. Essentially, the areas for implementation extend on the one hand to the positioning of the person (motion capture) and binauralization and on the other hand in the artistic sense to the design of the sound scene by positioning and synthesizing the sounds.

Figure 1

The motion capture in this project is realized with the Polhemus G4 system. The direction and position of a micro-sensor, which is attached to a pair of glasses worn by the participant, is determined by a magnetic field generated by two transmitters. A hub, which is connected to the micro-sensor via a cable, sends the motion capture data to a USB dongle connected to a laptop. This data is sent to another laptop, on which the binauralization takes place and which is ultimately connected to the wireless headphones.

Figure 2 shows two of the six objects in one variant each (angles of 45° and 90°). The next illustration (Fig. 3) shows the over-glasses (protective glasses that can also be worn over glasses) that are used in the sound installation. These goggles have a wide nose bridge to which the micro-sensor is attached with a micro-mount from Polhemus.

Figure 2

 

Figure 3

As previously explained, various decisions have to be made before the artistic aspect of the sound installation can be realized. This involves the positioning of the objects / sound sources and the sounds themselves.

Figure 4

 

Figure 5

Figure 4 shows a sketched top view of the complete structure. The six blue-colored circles mark the positions of the objects in the room and, of course, the sound sources of the scene in Binauralix, which can be seen in Figure 5. The direction and angle of the sources can be taken from the colorless areas (in Fig. 4), at either 45° or 90° angles, around the sound sources.

The completely wireless position detection and data transmission enables the participants to immerse themselves fully in this experience of the interactive reality-expanding sound world. The sound synthesis was carried out using the SuperCollider software. The sounds were mainly created through various tapping and clicking noises recorded by the SoundIn object, and finally changes and alienation of the sounds through amplitude and frequency modulation and various filters. By routing the sounds to a total of 6 output channels and “s.record(numChannels:6)”, I was able to create a two-minute multi-channel audio file in SuperCollider. When playing the file in Binauralix, the first channel is automatically mapped to source one, the second channel to source 2 and so on.

 

Technical implementation

The technical challenge for the implementation of the project initially consisted of receiving and reformatting the data from the sensor so that it could be used in Binauralix. The initial problem was that Binauralix is only available for MacOS and the software for the Polhemus G4 system is only available for Windows and Linux. As I had a MacBook and a laptop with Ubuntu Linux as my operating system at the time, I installed the Polhemus software for Linux.

After building and installing the Polhemus G4 software on Linux, the five applications “G4DevCfg”, “CreateSrcCfg”, “g4term”, “g4display” and “g4export” were available. For my project, all devices used must first be connected and configured with “G4DevCfg”. The terminal application “g4export” can be used to transmit the sensor data via UDP by specifying the previously created source configuration file, the local IP address of the receiver device and a port. The source configuration file is a file in which the position and orientation of the transmitter are defined by a “virtual frame of reference” and settings can be made for the entry hemisphere into the magnetic field, floor compensation and source calibration file. To run the application, the transmitters and the hub must be switched on at this point, the USB dongle must be connected to the laptop and the sensor to the hub, and the hub must be connected to the USB dongle. If the MacBook is now in the same network as the Linux laptop, the data can be received by specifying the previously used port. This is done with my sound installation in a self-created MaxMSP patch.

Figure 6

In this application, the appropriate port must first be selected on the left-hand side. As soon as the connection is established and the messages arrive, you can view them in raw form under the selection field. The six values that can be seen at the top in the middle of the application are the values for position and orientation that have been separated from the raw message. Final settings for the correct calibration can now be made in the action field below. There is also the option to mirror the axes individually or to change the Yaw value if unexpected problems should arise when setting up the sound installation. Once the values have been formatted into messages that can be used by Binauralix (visible at the bottom right of the application), they are sent to Binauralix.

The following videos provide a view of the scene in Binauralix and an auditory impression as the listener — driven by the sensor data — moves through the scene.

 

 

Past performances of the sound installation

The sound installation as a contribution to the EFFEKTE lecture series of the Wissenschaftsbüro-Karlsruhe

 

 

test
The sound installation as the subject of a workshop for the Kulturakademie at the HfM-Karlsruhe

 

ByLorenz Lehmann

Interactive composition/performance with live drawing and electronics

Foreword

In the following I would like to give an insight into the artistic and technical development of my piece “Waiting for the Night”. This article will be continuously updated and will thus document the development process.

The piece is to be realized by a performer and a draughtswoman.

 

Technical report

Setup

The performer stands on the stage. The projector must be positioned so that the image is projected onto the screen above the performer. There should be no shadow of the performer.

Fig. 1: Technical setup.

Sound analysis

Fig. 2: Spectral analysis in real time.

Live generated sound synthesis

Fig 3: CSound module

Live drawing

Artistic reflection

 

 

ByBrandon Snyder

Machine Learning in Music: One Application with Voice and Live Electronics

Up until this past August, my impressions of what machine learning could be used for was mostly functional, detached from any aesthetic reference point within my artistic practice. Cars recognizing stop signs, radiologists detecting malignant legions in tissue; these are the first things to come to my mind. There is definitely an art behind programming these tasks. However, it wasn’t clear to me yet how machine learning could relate to my world of contemporary concert music. Therefore, when I participated in Artemi-Maria Gioti’s machine learning workshop at impuls Academy 2021, my primary interest was to make personal artistic connections to this body of research, and to see what ways I could interrogate my underlying aesthetic assumptions in artistic applications of machine learning. The purpose of this text is to share with you the connections I made. I will walk through the composition process of my piece Shepherd for voice and live electronics, using it as a frame to touch upon basic machine learning theories and methods, as well as outline how I aesthetically reacted to them. I will not go deep into the technicalities of machine learning – there are far more qualified people than I for that specific task. However, I will say that the technical content of this blogpost is inspired heavily from Artemi-Maria Gioti, who led this workshop and whose research covers the creative applications of machine learning in a much deeper way. A further dive into the already rich world of machine learning and music can be begun at her website.

A fundamental definition of machine learning can be framed around the idea of improvement through experience. As computer scientist Tom M. Mitchell describes it, “The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.” (Mitchell, T. (1998). Machine Learning. McGraw-Hill.). This premise of ‘improvement’ already confronted me with non-trivial questions. For example, if machine learning is utilized to create an improvising duo partner, what exactly does the computer understand as ‘good’ or ‘bad’ improvisation, as it gains experience? Before even beginning to build a robust machine learning algorithm, answering this preliminary question is an entire undertaking in and of itself. In my piece Shepherd, the electronics were trained to recognize the sound of my voice, specifically whether I was whispering, talking, yelling, or being silent. However, my goal was not to create a perfectly accurate recognition algorithm. Rather, I wanted the effectiveness and the ineffectiveness of the algorithm to both play equal roles in achieving the piece’s concept. Shepherd is a performance piece takes after a metaphor from Jesus in the christian bible – sheep recognize a shepherd by the sound of their voice (John 10). The electronics reacts to my voice in a way that is simultaneously certain and uncertain. It is a reflection, through performance, on the nuances of spiritual faith, the way uncertainty necessarily partakes in the formation of conviction and belief. Here the electronics were not functional instrument (something designed to be controlled by my voice), but rather were functioning more as a second player (a duo partner, reacting to my voice with a level of unpredictability).

Concretely in the program, the electronics returned two separate answers for every input it is given (see figure 1). It gives a decisive, classification answer (“this is ‘silence’, this is ‘whispering’, this is ‘talking’, etc.), and it gives an indecisive, erratic answer via regression (‘silence: 0.833; whispering: 0.126; talking: 0.201; yelling: 0.044’). And important for this concept of conceiving belief through doubt, the classification answer is derived from the regression answer. The decisive answer (classification) was generally stable in its changes over time, while the indecisive answer (regression) moved more quickly and erratically. Overall, this provided a useful material for creating dynamic control of the actual digital sounds that the electronics produced. But before touching on the DSP, I want to outline how exactly these machine learning algorithms operate, how the electronics learn and evaluate the sound of my voice.

Figure 1: Max MSP and Wekinator (off-screen) analyze an audio’s MFCCs to give two outputs on the nature of the input audio. The first output is from a regression algorithm, the second is from a classification algorithm. 

In order for the electronics to evaluate my input voice, it first needs a training set, a collection of data extracted from audio of my voice, with which it could use to ‘learn’ my voice. An important technical point is that the machine learning algorithm never observes actual audio data. With training and testing data, the algorithm is always looking at numerical data (here called ‘descriptors’) extracted from the audio. This is one reason machine learning algorithms can work in realtime, even with audio. As I alluded to, my voice recognition program is underpinned by two machine learning concepts: classification and regression. A classification algorithm will return a discrete value from its input data. In my case, those values are ‘silence’, ‘whispering’, ’talking’, and ‘yelling’. To make a training set then, I recorded audio of each of these classes (4 audio files in total), and extracted MFCCs (Mel-Frequency Cepstrum Coefficients) from it. MFCC’s are a representation a sound’s spectral energy calibrated to the range of typical human auditory perception, and are already commonly used in speech recognition programs, music-information retrieval applications, and other applications based around timbre-recognition.

I used the Max MSP library Zsa.descriptors to calculate my MFCCs. I also experimented with other audio descriptors such as spectral centroid, spectral flatness, amplitude peaks, as well as varying numbers of MFCC’s. Eventually I discovered that my algorithm was most accurate when 13 MFCCs were the only descriptor, and that description data was taken only about five  times a second. I realized that, on a micro-level timescale, my four classes had a lot similarity. For example, the word ‘synthesizer,’ carried lots of ’s’ noise, which is virtually the same when whispered as when talked. Because of this, extracting data at an intentionally slower rate gave the algorithm a more general picture of each of my voice-classes, allowing these micro-moments of similarity to be smoothed out.

The standard algorithm used for my voice recognition concept was classification. However, my classification algorithm was actually built using a second common machine learning algorithm: regression. As I mentioned before, I wanted to build into my electronics a level of ‘indecision’, something erratic that would contrast the stable nature of a standard classification algorithm. Rather than returning discrete values, a regression algorithm gives a new ‘predictive’ value, based on a function derived from the training set data. In the context of my piece, the regression algorithm does not return a specific voice-class. Rather, it gives four percentage values, each corresponding to how close or far my input is to each of the four voice-classes. Therefore, though I may be whispering, the algorithm does not say whether I am whispering or not. It merely tells me how close or far away I am from the ‘whispering’ data that it has been trained on.

I used a regression algorithm in Wekinator, a simple and powerful machine learning tool, to build my model (see figure 2). Input audio was analyzed in Max MSP, and the descriptor data was sent via OSC to Wekinator. Wekinator built the predictive regression model from this data and then sent output back to Max MSP to be used for DSP control. In Max, I made my own version of a classification algorithm based on this regression data.

Figure 2: Wekinator is evaluating MFCC data from Max MSP and returning 4 values from 0.0-1.0, indicating the input’s similarity to the four voice classes (silence, whispering, talking, yelling). The evaluation is a regression model trained on 752 data samples. 

All this algorithm-building once again returns me to my original concern. How can I make an aesthetic connection with these concepts? As I mentioned, this piece, Shepherd is for my solo voice and live electronics. In the piece I stand alone on a stage, switching through different fictional personas (a speaker at a farming convention, a disgruntled restaurant chef, a compilation video of Danny Wolfers saying the word ‘synthesizer,’ and a preacher), and the electronics reacts to these different characters by switching through its own set of personas (sheep; a whispering, whimpering sous chef; a literal synthesizer; and a compilation of christian music). Both the electronics and I change our personas in reaction to each other. I exercise some level control over the electronics, but not total. As I said earlier, the performance of the piece is a reflection on the intertwinement of conviction and doubt, decision and indecision, within spiritual faith. Within this concept, the idea of a machine ‘improving’ towards ‘perfection’ is no longer an effective framework. In the concept, and consequently in the music I attempted to make, stable belief (classification) and unstable indecision (regression) were equal contributors towards the musical relationship between myself and the electronics.

Based on how my voice was classified, the electronics operated one of four DSP modules. The individual parameters of a given module were controlled by the erratic output data of the regression algorithm (see figure 3). For example, when my voice was classified as silent, a granular synthesizer would create textures of sheep-like noises. Within that synthesizer, the percentage levels of whispering and talking ‘detected’ within the silence would manipulate the pitch shifting in the synthesizer (see figure 4). In this way, the music was not just four distinct sound modules. The regression algorithm allowed for each module to bend and flex in certain directions, as my voice subtly suggested hints of one voice class from within another. For example, in one section I alternate rapidly between the persona of a farmer talking at a farming convention, and a chef frustratingly whispering at his sous chef. The electronics moved consequently between my whispering and talking DSP modules. But also, as my whispering became more frustrated and exasperated, the electronics would output higher levels of talking in its regression algorithm. Thus, the internal drama of my theatrical  performances is reacted to by the electronics.

Figure 3: The classification data would trigger one of four DSP modules. A given DSP module would receive the regression values for all four vocal classes. These four values would control the parameters of the DSP module.

Figure 4: Parameter window for granular synth triggered when the electronics classifies my voice as ‘silent’. The amount of whispering and talking detected in the silence would control the pitch of the grain. The amount of silence detected in the silence controlled the grain’s duration. Because this value is relatively static during actual silence from my voice, a level of artificial duration manipulation (seen a the top of the window) was programmed. 

I want to return to Tom Mitchell’s thesis that machine learning involves computer improving  automatically through experience. If Shepherd is a voice recognition tool, then it is inefficient at improvement. However, Shepherd was not conceived as a tool. Rather, creating Shepherd was more so a cultivation of a relationship between my voice and the electronics. The electronics were more of a duo partner, and less of an instrument. To put this more concretely, I was never looking for ‘accurate’ results from the machine. As I programmed, I was searching for results that illustrated Shepherd’s artistic concept of belief intertwined with doubt. In this way, ‘improving’ the piece did not mean improving the algorithm’s accuracy. It meant ‘improving’ the relationship between myself and the electronics. One positive from this approach is that the compositional process was never separated from the programming of the electronics. Both developed in tandem. The composing this piece brought me to the realization that creative applications of machine learning can be applied at every level of its discourse. If you ware interested in hearing a recording of this performance, a bootleg recording of the premiere can be found here.

https://youtu.be/LFQnpp5Uzbg

References:

  • Artemi-Maria Gioti – composer and artistic researcher working in the field of artificial intelligence. 
  • Wekinator – free, open-source software created by Rebecca Fiebrink that uses machine learning to create musical instruments, game interfaces, computervision, and other tools in sound and animation. 
  • Zsa.descriptors –  library for real-time sound descriptors analysis for Max MSP developed by Mikhail Malt and Emmanuel Jourdan.
  • NYU Music and Audio Research Laboratory – Free online resources and datasets.
  • AIMC – conference on artificial intelligence and musical creativity.
  • OM-Pursuit – Dictionary-based sound modelling for computer-aided composition in Open Music.