Instrument identification in polyphonic music signals based on individual partials
Jayme Garcia Arnal Barbedo,George Tzanetakis +1 more
- pp 401-404
TLDR
A new approach to instrument identification based on individual partials is presented, which makes identification possible even when the concurrently played instrument sounds have a high degree of spectral overlapping.Abstract:
A new approach to instrument identification based on individual partials is presented. It makes identification possible even when the concurrently played instrument sounds have a high degree of spectral overlapping. A pairwise comparison scheme which emphasizes the specific differences between each pair of instruments is used for classification. Finally, the proposed method only requires a single note from each instrument to perform the classification. If more than one partial is available the resulting multiple classification decisions can be summarized to further improve instrument identification for the whole signal. Encouraging classification results have been obtained in the identification of four instruments (saxophone, piano, violin and guitar).read more
Citations
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Proceedings ArticleDOI
Classifying musical instruments using speech signal processing methods
TL;DR: Among the features used, after MFCC, ZCR proved to be the optimal feature for the classification of drum instrument, and the most significant feature for classifying Guitar, Violin and Drum is MFCC as it gives the better accurate results.
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Overlapped soundtracks segmentation using singular spectrum analysis and random forests
Duraid Y. Mohammed,Francis F. Li +1 more
TL;DR: The classification performance of overlapped soundtracks is effectively improved and singular spectrum analysis has been found to be an efficient way to discriminate speech/music in mixed soundtracks.
Book ChapterDOI
A Multiple-Expert Framework for Instrument Recognition
TL;DR: A new approach towards feature-based instrument recognition is presented that makes use of redundancies in the harmonic structure and temporal development of a note that is targeted at transferability towards use on polyphonic material.
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A feature relevance study for guitar tone classification
TL;DR: It turns out, that a selection of 505 features out of the full feature set of 1155 elements does only reduce the recognition rate of a linear SVM from 82% to 78% and with the use of a polynomial instead of alinear kernel the recognition rates with the reduced feature set can even be increased to 84%.
Book ChapterDOI
Stratification of String Instruments Using Chroma-Based Features
TL;DR: The proposed work relies on Chroma-based low-dimensional feature vector to categorize String instruments, which is an octave independent estimation of strength of all possible notes in Western 12 note scale at different points of time.
References
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Proceedings Article
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TL;DR: This paper proposes a framework for the sound source separation and timbre classification of polyphonic, multi-instrumental music signals, inspired by ideas from Computational Auditory Scene Analysis and formulated as a graph partitioning problem.
Proceedings Article
The Importance of Cross Database Evaluation in Sound Classification
Arie Livshin,Xavier Rodet +1 more
TL;DR: It is shown that "self classification" is not necessarily a good statistic for the ability of a classification algorithm to learn, generalize or classify well, and the alternative "Minus-1 DB" evaluation method is introduced that does not have the shortcomings of " self classification.
Proceedings Article
Automatic Instrument Recognition in a Polyphonic Mixture Using Sparse Representations.
TL;DR: A method to address the automatic instrument recognition in polyphonic music is introduced, based on the decomposition of the music signal with instrument-specific harmonic atoms, yielding to an approximate object representation of the signal.
Journal ArticleDOI
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TL;DR: A new decisively important factor in both the perceptual and the automated piano-guitar identification process is introduced, determined by the nontonal spectral content of a note, while it is, in practice, totally independent of the note spectrum tonal part.
Journal ArticleDOI
Discriminating Between Pitched Sources in Music Audio
TL;DR: This article provides a derivation of a suboptimal subset out of a wide range of common audio features that maximizes the potential to discriminate between pitched sources in polyphonic music and an estimation of the improvement in accuracy that can be achieved by using features other than pitch in the grouping process.