Dan Gang

... Modulating Jitter on Higher Order Spectra with Applications to Sound Synthesis and Classi cations - Shlomo Dubnov, Naftali Tishby and Dalia Cohen. NetNeg: A Hybrid Interactive Ar-chitecture for Composing Polyphonic Music in Real Time - Claudia V. Gold-man, Dan Gang ...

In our previous work we proposed a theory of cognition of tonal music based on control of expectations and created a model to test the theory using a hierarchical sequential neural network. The net learns metered and rhythmecized functional tonal harmonic progressions allowing us to measure uctuations in the degree of realized expectation (DRE). Preliminary results demonstrated the necessity of including metric information in the model in order to obtain more realistic results for the model of the DRE. This was achieved by adding two units representing periodic index of meter to the input layer. In this paper we describe signi cant extensions to the architecture. Speci cally, our goal was to represent more general meter tracking strategies and consider their implications as cognitive models. The output layer of the sub-net for metric information is fully connected to the hidden layer of sequential net. This output layer includes pools of three and four units representing duple and t...

We built a system that allows musical performers (and listeners) who wish to play together to organize into multiple session groups. The users interact in real time over the network, and may dynamically join or leave a session group. The players contribute to the session by playing on their MIDI controllers, using General MIDI protocol. We assume a totally asynchronous environment in which failures of both end systems and communication links are possible. TransMIDI was implemented using the Transis group communication system for fault tolerance and coordination. Transis is a transport layer that supports e cient and reliable multicast and membership services. The advanced Transis group services allow for di erent forms of cooperation among performers, so that many di erent kinds of musical ensembles can be easily established. Moreover, TransMIDI provides its users with means for exploring novel ways of musical interaction.

We describe a sequential neural network for harmonizing melodies in real time. It models aspects of human cognition. This neural network succeeds reasonably well, if we take into consideration the constraints imposed by real time processing. The model exploits e ciently the available sequential information. The net contains a sub-net for meter that produces a periodic index of meter, providing the needed metric awareness. The net learns the relations between important notes of the melody and their harmonies and is able to produce harmonies for new melodies in real time, i.e., without knowledge of the future development of the melody.

In this paper a model of musical listening is described. The model provides a visualization of the formulation and realization of musical expectations as a listener hears (or imagines) functional tonal music. The model provides a framework for categorizing and evaluating expectations and their associated realizations. The model is a modular sequential recurrent neural network that incorporates interdependent sub-nets for harmonic progressions and meter. Using this model we examine the interplay of metric inference and functional tonal harmony. The model visualizes the mutual influence of these musical characteristics and provides visualizations of normative and disruptive listening situations. In addition to visualizing processes of expectations and realization a number of cognitive implications are discussed. Musical listening involves sub-symbolic learning through experience. Acquiring metric and harmonic schema are an emergent property of a listener's exposure to metered tona...

There are musical activities in which we are faced with symbolic and sub-symbolic processes. This research focuses on the question whether there is any advantage in integrating a neural network together with a distributed artiicial intelligence approach in the musical domain. In this work, we present a new approach for composing and analyzing polyphonic music. As a case study, we began experimenting with rst species two-part counterpoint melodies. Our system design is inspired by the cognitive process of a human musician. We have developed a hybrid system composed of a connectionist module and an agent-based module to combine the symbolic and sub-symbolic levels to achieve this task. The network produces aesthetic melodies based on the training examples it was given. The agents choose which are the next notes in the two-part melodies by negotiating over the possible combinations of notes suggested by the network .

There are musical activities in which we are faced with symbolic and sub-symbolic processes. This research focuses on the question whether integrating a neural network together with a distributed artiicial intelligence approach has any advantages in the music domain. In this work, we present a new approach for composing and analyzing poliphonic music. As a case study, we began experimenting with rst species two-part counterpoint melodies. Our system design is inspired by the cognitive process of a human musician. We have developed a hybrid system composed of a connectionist module and an agent-based module to combine the symbolic and sub-symbolic levels to achieve this task. The network produces aesthetic melodies based on the training examples it was given. The agents choose which are the next notes in the two-part melodies by negotiating over the possible combinations of notes suggested by the network.

We describe a sequential neural network for harmonizing melodies in real-time. The network models aspects of human cognition and can be used as the basis for building an interactive system that automatically generates accompaniment for simple melodies in live performance situations. The net learns relations between important notes of the melody and their harmonies and is able to produce harmonies for new melodies in real-time, that is, without advanced knowledge of the continuation of the melody. We tackle the challenge of evaluating these harmonies by applying distance functions to measure the disparity between the net's choice of a chord and that of the author of the source book from which the melody was taken. We experimented with three major issues that have implications on the performance of the model: searching for the best learning parameters (e.g., the decay parameters), the size of the learning set and the in uence of metric information. The decay parameters set the sco...

We describe a model of music cognition basedon fluctuations in the degree of realized expectation(DRE) in which we employ a neural networkwhich receives representations of standardand anomalous chord progressions derived fromopening periods of piano sonatas by Mozart andHaydn. In order to account for essential metricinformation we incorporate sequential datawith temporal information, by integrating a subnetworkwith the sequential net. This sub-netimposes

... Modulating Jitter on Higher Order Spectra with Applications to Sound Synthesis and Classi cations - Shlomo Dubnov, Naftali Tishby and Dalia Cohen. NetNeg: A Hybrid Interactive Ar-chitecture for Composing Polyphonic Music in Real Time - Claudia V. Gold-man, Dan Gang ...

We describe a series of experiments using sequential neural networks to model the effect of contextual bias in music cognition. The model quantifies the strength and specificity of a virtual listener's expectations while listening to functional tonal harmonic chord sequences. The network integrates pools of duple and triple metric units with pitch class representations of chords. The 'listener' is then

We describe a sequential neural network for harmonizing melodies in real time. It models aspects of human cognition. This neural network succeeds reasonably well, if we take into consideration the constraints imposed by real time processing. The model exploits efficiently the available sequential information. The net contains a sub-net for meter that produces a periodic index of meter, providing the

We describe a sequential neural network for harmonizing melodies in real-time. The networkmodels aspects of human cognition and can be used as the basis for building an interactive systemthat automatically generates accompaniment for simple melodies in live performance situations.The net learns relations between important notes of the melody and their harmonies and is ableto produce harmonies for new melodies in

... of tonal music based on control of expec-tations and created a model to test the theory using a hierarchical sequential neural network. ... Furthermore, our model enables the integration of mutual influences of two interrelated aspects of musical expectations: schematic ...

... The model is hybrid in that we use modular sub-networks to simulate the distinct yet mutually influential schemas involved in constructing expectations for sequential events and the temporal cyclical grid that creates metrical support for these expectations. ...