DMCA
Unsupervised incremental learning and prediction of audio signals (2010)
| Venue: | in Proceedings of 20th International Symposium on Music Acoustics |
| Citations: | 1 - 1 self |
BibTeX
@INPROCEEDINGS{Marxer10unsupervisedincremental,
author = {Ricard Marxer and Hendrik Purwins},
title = {Unsupervised incremental learning and prediction of audio signals},
booktitle = {in Proceedings of 20th International Symposium on Music Acoustics},
year = {2010}
}
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Abstract
The artful play with the listener’s expectations is one of the supreme skills of a gifted musician. We present a system that analyzes an audio signal in an unsupervised manner in order to generate a musical representation of it on-the-fly. The system performs the task of next note prediction using the emerged representation. The main difference between our system and other existing music prediction systems is the fact that it dynamically creates the necessary representations as needed. Therefore it can adapt itself to any type of sounds, with as many timbre classes as there may be. The system consists of a conceptual clustering algorithm coupled with a modified hierarchical N-gram. The main flow of the system can be summarized in the following processing steps: 1) segmentation by transient detection, 2) timbre representation of each segment by Mel-cepstrum coefficients, 3) discretization by conceptual clustering, yielding a number of different sound classes (e.g. instruments) that can incrementally grow or shrink depending on the context resulting in a discrete sequence of sound events, 4) extraction of statistical regularities using hierarchical N-grams (Pfleger 2002), 5) prediction of continuation, and 6) sonification. The system is tested on voice recordings. We assess the robustness of the performance with respect to complexity and noise of the signal. Given that the number of estimated timbre classes is not necessarily the same as in the ground truth, we propose a performance measure (F-recall) based on pairwise matching. Finally, we sonify the predicted sequence in order to evaluate the system from a qualitative point of view. We evaluate separately the
Keyphrases
audio signal incremental learning mel-cepstrum coefficient next note prediction supreme skill modified hierarchical n-gram timbre representation musical representation sound event different sound class necessary representation hierarchical n-grams following processing step voice recording artful play conceptual clustering pairwise matching performance measure music prediction system many timbre class ground truth discrete sequence main flow predicted sequence timbre class unsupervised manner statistical regularity qualitative point listener expectation conceptual clustering algorithm gifted musician transient detection main difference emerged representation
