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Instrument identification in polyphonic music signals based on individual partials

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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).

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Citations
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Proceedings ArticleDOI

Classifying musical instruments using speech signal processing methods

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Overlapped soundtracks segmentation using singular spectrum analysis and random forests

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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.
Proceedings Article

A feature relevance study for guitar tone classification

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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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Journal ArticleDOI

A tutorial on onset detection in music signals

TL;DR: Methods based on the use of explicitly predefined signal features: the signal's amplitude envelope, spectral magnitudes and phases, time-frequency representations, and methods based on probabilistic signal models are discussed.
Journal ArticleDOI

Multiple fundamental frequency estimation based on harmonicity and spectral smoothness

TL;DR: The spectral smoothness principle is proposed as an efficient new mechanism in estimating the spectral envelopes of detected sounds and works robustly in noise, and is able to handle sounds that exhibit inharmonicities.

Development of the RWC Music Database

TL;DR: The RWC (Real World Computing) Music Database is introduced, a copyright-cleared music database that is available to researchers as a common foundation for research and has already been widely used.
Journal ArticleDOI

Instrument recognition in polyphonic music based on automatic taxonomies

TL;DR: This study focuses on a single music genre but combines a variety of instruments among which are percussion and singing voice, and obtains a taxonomy of musical ensembles which is used to efficiently classify possible combinations of instruments played simultaneously.
Journal ArticleDOI

Temporal Integration for Audio Classification With Application to Musical Instrument Classification

TL;DR: A number of methods for early and late temporal integration are proposed and an in-depth experimental study on their interest for the task of musical instrument recognition on solo musical phrases is provided.
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