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.
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).
Citations
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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
11 Nov 2022
TL;DR: In this article , a system was presented to distinguish bass grooves in the presence of drums using MFCC-based features and achieved an accuracy of 97.38% using multi-layer perceptron (MLP)-based classification.
Abstract: Technology has found its way into disparate spheres of music, from production to performance. Researchers have attempted to automate multitudinous aspects in this domain. One of the interests has been towards the automated generation of music pieces itself. Bass grooves are an integral part of most music pieces. It makes a piece sound complete and bridges the gap between the percussion and melody sections. Thus, it is essential for machines to understand bass grooves for automated music analysis and production. Automatically distinguishing bass grooves is difficult and it aggravates even more for polyphonic music. In polyphonic music, the bass grooves tend to be at a lower volume and its frequency range has profound overlap with the percussion section which contributes to the complexity of identification. In this paper, a system is presented to distinguish bass grooves in the presence of drums. Experiments were performed with 7 grooves totaling 4473 clips which were modeled using MFCC-based features. The highest accuracy of 97.38% was obtained using multi-layer perceptron (MLP)-based classification.
Proceedings Article•
01 Jan 2008
TL;DR: In this paper, a flexible method to classify musical audio signals automatically into musical genres is introduced, which consists in assigning up to three genres to a given signal, taking into account that some musical signals can have elements from more than one genre.
Abstract: A flexible method to classify musical audio signals automatically into musical genres is introduced. The flexibility consists in assigning up to three genres to a given signal, taking into account that some musical signals can have elements from more than one genre. The assigned genres are classified according to their influence over the signal. The technique divided the signals into 21.3-ms frames, from which four features are extracted. Some statistical results concerning the extracted features are collected along 1-s analysis segments, resulting in a 12-element vector by segment. Such vectors feed a classification procedure that 1) uses a wide and deep taxonomy, allowing a meticulous comparison of different genres; 2) performs a pairwise comparison of genres, which allows emphasizing the differences between each pair of genres; 3) assigns, according to the information collected, the degree of influence of each genre over the signal: none, weak, or strong; and 4) performs the final classification, which is given by the genres that best fit the attributes of the signal together with their respective degrees of influence. This procedure makes it possible to treat adequately the huge amount of songs influenced by or resulting from the fusion of two or more musical genres. The approach has achieved good results with a relatively low computational effort.
References
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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.
802 citations
"Instrument identification in polyph..." refers background in this paper
...Index Terms— instrument identification, polyphonic musical signals, pairwise comparison, partial-based classification...
[...]
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.
356 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.
189 citations
"Instrument identification in polyph..." refers background in this paper
...Index Terms— instrument identification, polyphonic musical signals, pairwise comparison, partial-based classification...
[...]
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%.
133 citations
"Instrument identification in polyph..." refers background in this paper
...Index Terms— instrument identification, polyphonic musical signals, pairwise comparison, partial-based 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.
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.
129 citations
"Instrument identification in polyph..." refers background in this paper
...Index Terms— instrument identification, polyphonic musical signals, pairwise comparison, partial-based classification...
[...]