Journal ArticleDOI
A connectionist approach to automatic transcription of polyphonic piano music
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A connectionist approach to automatic transcription of polyphonic piano music with a new partial tracking technique, based on a combination of an auditory model and adaptive oscillator networks, and shows how synchronization of adaptive oscillators can be exploited to track partials in a musical signal.Abstract:
In this paper, we present a connectionist approach to automatic transcription of polyphonic piano music. We first compare the performance of several neural network models on the task of recognizing tones from time-frequency representation of a musical signal. We then propose a new partial tracking technique, based on a combination of an auditory model and adaptive oscillator networks. We show how synchronization of adaptive oscillators can be exploited to track partials in a musical signal. We also present an extension of our technique for tracking individual partials to a method for tracking groups of partials by joining adaptive oscillators into networks. We show that oscillator networks improve the accuracy of transcription with neural networks. We also provide a short overview of our entire transcription system and present its performance on transcriptions of several synthesized and real piano recordings. Results show that our approach represents a viable alternative to existing transcription systems.read more
Citations
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Book ChapterDOI
Summary and Discussion
TL;DR: This chapter presents the summary and the discussion of this book and database, in which the UMA-Piano chord music Data Base is presented as an engineering point of view of harmony.
Journal ArticleDOI
Multipitch Estimation of Piano Sounds Using a New Probabilistic Spectral Smoothness Principle
TL;DR: A new method for the estimation of multiple concurrent pitches in piano recordings is presented, which addresses the issue of overlapping overtones by modeling the spectral envelope of the overtones of each note with a smooth autoregressive model.
Journal ArticleDOI
Adaptive Harmonic Spectral Decomposition for Multiple Pitch Estimation
TL;DR: A NMF-like algorithm is derived that performs similarly to supervised NMF using pre-trained piano spectra but improves pitch estimation performance by 6% to 10% compared to alternative unsupervised NMF algorithms.
Journal ArticleDOI
A discriminative model for polyphonic piano transcription
TL;DR: A discriminative model for polyphonic piano transcription is presented and a frame-level transcription accuracy of 68% was achieved on a newly generated test set, and direct comparisons to previous approaches are provided.
Journal ArticleDOI
Signal Processing for Music Analysis
TL;DR: It is demonstrated that, to be successful, music audio signal processing techniques must be informed by a deep and thorough insight into the nature of music itself.
References
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TL;DR: The authors present a time-delay neural network (TDNN) approach to phoneme recognition which is characterized by two important properties: using a three-layer arrangement of simple computing units, a hierarchy can be constructed that allows for the formation of arbitrary nonlinear decision surfaces, which the TDNN learns automatically using error backpropagation.
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Phoneme recognition using time-delay neural networks
TL;DR: In this article, the authors presented a time-delay neural network (TDNN) approach to phoneme recognition, which is characterized by two important properties: (1) using a three-layer arrangement of simple computing units, a hierarchy can be constructed that allows for the formation of arbitrary nonlinear decision surfaces, which the TDNN learns automatically using error backpropagation; and (2) the time delay arrangement enables the network to discover acoustic-phonetic features and the temporal relationships between them independently of position in time and therefore not blurred by temporal shifts in the input
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Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps
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The computer music tutorial
TL;DR: The Computer Music Tutorial is a comprehensive text and reference that covers all aspects of computer music, including digital audio, synthesis techniques, signal processing, musical input devices, performance software, editing systems, algorithmic composition, MIDI, synthesizer architecture, system interconnection, and psychoacoustics.