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Proceedings ArticleDOI

Instrument identification in polyphonic music signals based on individual partials

14 Mar 2010-pp 401-404

TL;DR: 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.

AbstractA 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
01 Dec 2016
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.
Abstract: Identification of musical instruments from the acoustic signal using speech signal processing methods is a challenging problem. Further, whether this identification can be carried out by a single musical note, like humans are able to do, is an interesting research issue that has several potential applications in the music industry. Attempts have been made earlier using the spectral and temporal features of the music acoustic signals. The process of identifying the musical instrument from monophonic audio recording basically involves three steps — pre-processing of music signal, extracting features from it and then classifying those. In this paper, we present an experiment-based comparative study of different features for classifying few musical instruments. The acoustic features, namely, the Mel-Frequency Cepstral Coefficients (MFCCs), Spectral Centroids (SC), Zero-Crossing Rate (ZCR) and signal energy are derived from the music acoustic signal using different speech signal processing methods. A Support Vector Machine (SVM) classifier is used with each feature for the relative comparisons. The classification results using different combinations of training by features from different music instrument and testing with another/same type of music instruments are compared. Our results indicate that the most significant feature for classifying Guitar, Violin and Drum is MFCC as it gives the better accurate results. Also, the feature which gives better accuracy results for the drum instrument is ZCR. Among the features used, after MFCC, ZCR proved to be the optimal feature for the classification of drum instrument.

11 citations


Book ChapterDOI
15 Oct 2013
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.
Abstract: Instrument recognition is an important task in music information retrieval (MIR). Whereas the recognition of musical instruments in monophonic recordings has been studied widely, the polyphonic case still is far from being solved. 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. The structure of the proposed method is targeted at transferability towards use on polyphonic material. Multiple feature categories are extracted and classified separately with SVM models. In a further step, class probabilities are aggregated in a two-step combination scheme. The presented system was evaluated on a dataset of 3300 isolated single notes. Different aggregation methods are compared. As the results of the joined classification outperform individual categories, further development of the presented technique is motivated.

3 citations


Cites methods from "Instrument identification in polyph..."

  • ...This concept has been applied by classifying instruments based on individual partials by Barbedo & Tzanetakis in [1]....

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Proceedings Article
01 Jan 2012
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%.
Abstract: A series of experiments on the automatic classification of classical guitar sounds with support vector machines has been carried out to investigate the relevance of the features and to minimise the feature set for successful classification. Features used for classification were the time series of the partial tone amplitudes, and of the MFCCs, and the energy distribution of the nontonal percussive sound that is produced in the attack phase of the tone. Furthermore the influence of sound parameters as timbre, player, fret position and string number on the recognition rate is investigated. Finally, several nonlinear kernels are compared in their classification performance. 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%. With the use of a polynomial instead of a linear kernel the recognition rate with the reduced feature set can even be increased to 84%.

3 citations


Cites background from "Instrument identification in polyph..."

  • ...Barbedo and Tsanetakis [2] published their results on the even more challenging task of instrument classification in polyphonic recordings....

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Book ChapterDOI
01 Jan 2019
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.
Abstract: Identification of instrument type from acoustic signal is a challenging issue. It is also an interesting and popular research area having several promising applications in music industry. Researchers have already been able to classify instruments into several broad categories like String, Woodwind, Percussion, and Keyboard etc. using acoustic features like Mel-Frequency Cepstral Coefficients (MFCCs), Zero Crossing Rate (ZCR) etc. MFCC has been found to be excessively used. In this work an alternative acoustic feature of MFCC has been proposed. Chroma is an octave independent estimation of strength of all possible notes in Western 12 note scale at different points of time. Sound envelope originated by a note reflects the signature of an instrument and this can be used to stratify String instruments into various categories. The proposed work relies on Chroma-based low-dimensional feature vector to categorize String instruments. For classification purpose, simple and popular classifiers like Neural Network, k-NN, Naive Bayes’ have been exercised.

1 citations


Journal ArticleDOI
TL;DR: A hierarchical scheme is proposed to classify string instruments without using MFCC-based features, and a neural network, k-NN, Naive Bayes' and Support Vector Machine have been used to classify.
Abstract: Automatic recognition of instrument types from an audio signal is a challenging and a promising research topic. It is challenging as there has been work performed in this domain and because of its applications in the music industry. Different broad categories of instruments like strings, woodwinds, etc., have already been identified. Very few works have been done for the sub-categorization of different categories of instruments. Mel Frequency Cepstral Coefficients (MFCC) is a frequently used acoustic feature. In this work, a hierarchical scheme is proposed to classify string instruments without using MFCC-based features. Chroma reflects the strength of notes in a Western 12-note scale. Chroma-based features are able to differentiate from the different broad categories of string instruments in the first level. The identity of an instrument can be traced through the sound envelope produced by a note which bears a certain pitch. Pitch-based features have been considered to further sub-classify string instruments in the second level. To classify, a neural network, k-NN, Naive Bayes' and Support Vector Machine have been used.

1 citations


References
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Journal ArticleDOI
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.
Abstract: Note onset detection and localization is useful in a number of analysis and indexing techniques for musical signals. The usual way to detect onsets is to look for "transient" regions in the signal, a notion that leads to many definitions: a sudden burst of energy, a change in the short-time spectrum of the signal or in the statistical properties, etc. The goal of this paper is to review, categorize, and compare some of the most commonly used techniques for onset detection, and to present possible enhancements. We discuss 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: model-based change point detection, surprise signals, etc. Using a choice of test cases, we provide some guidelines for choosing the appropriate method for a given application.

752 citations


"Instrument identification in polyph..." refers background in this paper

  • ...Index Terms— instrument identification, polyphonic musical signals, pairwise comparison, partial-based classification...

    [...]


Journal ArticleDOI
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.
Abstract: A new method for estimating the fundamental frequencies of concurrent musical sounds is described. The method is based on an iterative approach, where the fundamental frequency of the most prominent sound is estimated, the sound is subtracted from the mixture, and the process is repeated for the residual signal. For the estimation stage, an algorithm is proposed which utilizes the frequency relationships of simultaneous spectral components, without assuming ideal harmonicity. For the subtraction stage, the spectral smoothness principle is proposed as an efficient new mechanism in estimating the spectral envelopes of detected sounds. With these techniques, multiple fundamental frequency estimation can be performed quite accurately in a single time frame, without the use of long-term temporal features. The experimental data comprised recorded samples of 30 musical instruments from four different sources. Multiple fundamental frequency estimation was performed for random sound source and pitch combinations. Error rates for mixtures ranging from one to six simultaneous sounds were 1.8%, 3.9%, 6.3%, 9.9%, 14%, and 18%, respectively. In musical interval and chord identification tasks, the algorithm outperformed the average of ten trained musicians. The method works robustly in noise, and is able to handle sounds that exhibit inharmonicities. The inharmonicity factor and spectral envelope of each sound is estimated along with the fundamental frequency.

346 citations


"Instrument identification in polyph..." refers background in this paper

  • ...Index Terms— instrument identification, polyphonic musical signals, pairwise comparison, partial-based classification...

    [...]


01 Jan 2004
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.
Abstract: In this paper I introduce the RWC (Real World Computing) Music Database, a copyright-cleared music database (DB) that is available to researchers as a common foundation for research. Shared DBs are common in other research fields and have made significant contributions to progress in those fields. The field of music information processing, however, has lacked a common DB of musical pieces or a large-scale DB of musical instrument sounds. The RWC Music Database was therefore built as the world’s first large-scale music DB compiled specifically for research purposes. It contains six original component DBs: the Popular Music Database, Royalty-Free Music Database, Classical Music Database, Jazz Music Database, Music Genre Database, and Musical Instrument Sound Database. The DB has been distributed to researchers at a nominal cost to cover only duplication, shipping, and handling charges (i.e., it is practically free), and has already been widely used.

180 citations


"Instrument identification in polyph..." refers background in this paper

  • ...Index Terms— instrument identification, polyphonic musical signals, pairwise comparison, partial-based classification...

    [...]


Journal ArticleDOI
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.
Abstract: We propose a new approach to instrument recognition in the context of real music orchestrations ranging from solos to quartets. The strength of our approach is that it does not require prior musical source separation. Thanks to a hierarchical clustering algorithm exploiting robust probabilistic distances, we obtain a taxonomy of musical ensembles which is used to efficiently classify possible combinations of instruments played simultaneously. Moreover, a wide set of acoustic features is studied including some new proposals. In particular, signal to mask ratios are found to be useful features for audio classification. This study focuses on a single music genre (i.e., jazz) but combines a variety of instruments among which are percussion and singing voice. Using a varied database of sound excerpts from commercial recordings, we show that the segmentation of music with respect to the instruments played can be achieved with an average accuracy of 53%.

131 citations


"Instrument identification in polyph..." refers background in this paper

  • ...Index Terms— instrument identification, polyphonic musical signals, pairwise comparison, partial-based classification...

    [...]


Journal ArticleDOI
C. Joder1, Slim Essid1, Gael Richard1
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.
Abstract: Nowadays, it appears essential to design automatic indexing tools which provide meaningful and efficient means to describe the musical audio content. There is in fact a growing interest for music information retrieval (MIR) applications amongst which the most popular are related to music similarity retrieval, artist identification, musical genre or instrument recognition. Current MIR-related classification systems usually do not take into account the mid-term temporal properties of the signal (over several frames) and lie on the assumption that the observations of the features in different frames are statistically independent. The aim of this paper is to demonstrate the usefulness of the information carried by the evolution of these characteristics over time. To that purpose, we propose a number of methods for early and late temporal integration and provide an in-depth experimental study on their interest for the task of musical instrument recognition on solo musical phrases. In particular, the impact of the time horizon over which the temporal integration is performed will be assessed both for fixed and variable frame length analysis. Also, a number of proposed alignment kernels will be used for late temporal integration. For all experiments, the results are compared to a state of the art musical instrument recognition system.

119 citations


"Instrument identification in polyph..." refers background in this paper

  • ...Index Terms— instrument identification, polyphonic musical signals, pairwise comparison, partial-based classification...

    [...]