Musical Instrument Timbres Classification with Spectral Features

Agostini, Giulio; Longari, Maurizio; Pollastri, Emanuele

doi:10.1155/s1110865703210118

Cited by 87 publications

(50 citation statements)

References 24 publications

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“…Some approaches take into account only partial frequencies (like Agostini et al [94,95]), while others also consider partial energies and bandwidths (see Cai et al [96]). …”

Section: Tonality-related Physical Frequency Featuresmentioning

confidence: 99%

A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds

2016

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Endowing machines with sensing capabilities similar to those of humans is a prevalent quest in engineering and computer science. In the pursuit of making computers sense their surroundings, a huge effort has been conducted to allow machines and computers to acquire, process, analyze and understand their environment in a human-like way. Focusing on the sense of hearing, the ability of computers to sense their acoustic environment as humans do goes by the name of machine hearing. To achieve this ambitious aim, the representation of the audio signal is of paramount importance. In this paper, we present an up-to-date review of the most relevant audio feature extraction techniques developed to analyze the most usual audio signals: speech, music and environmental sounds. Besides revisiting classic approaches for completeness, we include the latest advances in the field based on new domains of analysis together with novel bio-inspired proposals. These approaches are described following a taxonomy that organizes them according to their physical or perceptual basis, being subsequently divided depending on the domain of computation (time, frequency, wavelet, image-based, cepstral, or other domains). The description of the approaches is accompanied with recent examples of their application to machine hearing related problems.

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“…Some approaches take into account only partial frequencies (like Agostini et al [94,95]), while others also consider partial energies and bandwidths (see Cai et al [96]). …”

Section: Tonality-related Physical Frequency Featuresmentioning

confidence: 99%

A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds

2016

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“…Brown 26 used a broadly similar method, building a classifier from Gaussian probability density functions acted upon by a Bayesian decision rule. Agostini et al 47 experimented with classifiers built from support vector machines (SVM). Many other approaches can be found in the literature.…”

Section: B Temporal Recurrent Reservoir Network Classifier (Echo Stamentioning

confidence: 99%

A neurally inspired musical instrument classification system based upon the sound onset

Newton

Smith

2012

The Journal of the Acoustical Society of America

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Physiological evidence suggests that sound onset detection in the auditory system may be performed by specialized neurons as early as the cochlear nucleus. Psychoacoustic evidence shows that the sound onset can be important for the recognition of musical sounds. Here the sound onset is used in isolation to form tone descriptors for a musical instrument classification task. The task involves 2085 isolated musical tones from the McGill dataset across five instrument categories. A neurally inspired tone descriptor is created using a model of the auditory system's response to sound onset. A gammatone filterbank and spiking onset detectors, built from dynamic synapses and leaky integrate-and-fire neurons, create parallel spike trains that emphasize the sound onset. These are coded as a descriptor called the onset fingerprint. Classification uses a time-domain neural network, the echo state network. Reference strategies, based upon mel-frequency cepstral coefficients, evaluated either over the whole tone or only during the sound onset, provide context to the method. Classification success rates for the neurally-inspired method are around 75%. The cepstral methods perform between 73% and 76%. Further testing with tones from the Iowa MIS collection shows that the neurally inspired method is considerably more robust when tested with data from an unrelated dataset.

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“…It has both scientific and practical applications. Although it has been considered as difficult problem, some approaches dealing with single instrument identification have recently been developed such as using Cepstral coefficient [1], Temporal features [2], Spectral features [3]. For more difficult problem which is to identify the multi-instrumental polyphonic music, some previous research has been done such as: Using frequency component adaptation with given correct F0s [4].…”

Section: Introductionmentioning

confidence: 99%

Musical Instrument Identification Based on New Boosting Algorithm with Probabilistic Decisions

Sagayama

2012

Lecture Notes in Computer Science

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Musical Instrument Timbres Classification with Spectral Features

Cited by 87 publications

References 24 publications

A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds

A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds

A neurally inspired musical instrument classification system based upon the sound onset

Musical Instrument Identification Based on New Boosting Algorithm with Probabilistic Decisions

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