Automated classification of piano-guitar notes

Fragoulis, D.; Papaodysseus, C.; Exarhos, M.; Roussopoulos, G.; Panagopoulos, T.; Kamarotos, Dimitris

doi:10.1109/tsa.2005.857571

Cited by 16 publications

(4 citation statements)

References 13 publications

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“…Fragoulis [13] also verified that NAS contains more timbre information than tonal part does. The nontonal content extraction first spots each partial within a region that covers the whole harmonic spectral peak.…”

Section: Feature Extraction and Evaluationmentioning

confidence: 79%

Music Instrument Classification using Nontonal MFCC

Wu¹,

Wang²,

Liu³

2017

Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017

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Abstract. Combined with the sounding mechanism and cepstrum, a new model is proposed to describe the timbre more precisely, together with the nontonal Mel-frequency cepstral coefficients (NMFCC) derived from the nontonal spectral content which relates closely to the resonator. A better performance is observed from the experiment results of five classifiers over the isolated instrument samples of 13 instruments of different instrument families. The NMFCC method outperforms the one using MFCC with an accuracy rate of 97.7% for individual instruments.

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Section: Feature Extraction and Evaluationmentioning

confidence: 79%

Music Instrument Classification using Nontonal MFCC

Wu¹,

Wang²,

Liu³

2017

Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017

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“…The participants also heard different-timbre trials, in which the same melody was played but the timbre changed. For one-third of the participants, the two timbres were very similar (piano and guitar; see Fragoulis et al, 2006), moderately similar (bassoon and alto saxophone), or distinct (vibraphone and muted trumpet). The timbre distinctiveness estimates for the latter two pairs were drawn from Iverson and Krumhansl (1993).…”

Section: Methodsmentioning

confidence: 94%

The familiar-melody advantage in auditory perceptual development: Parallels between spoken language acquisition and general auditory perception

Creel

2019

Atten Percept Psychophys

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How do learners build up auditory pattern knowledge? Findings from children's spoken word learning suggest more robust auditory representations for highly familiar words than for newly learned words. This argues against spoken language learning as a process of simply acquiring a fixed set of speech sound categories, suggesting instead that specific words may be the relevant units. More generally, one might state this as the specific-learning hypothesis-that acquiring sound pattern knowledge involves learning specific patterns, rather than abstract pattern components. To understand the nature of human language knowledge, it is important to determine whether this specific learning reflects processes unique to spoken language learning or instead reflects more general auditory-learning processes. To investigate whether the specific-learning hypothesis extends to auditory pattern learning more generally, the present study tested the perceptual processing of familiar melodies versus carefully matched unfamiliar melodies. Children performed better at both audiovisual mapping (Exp. 1) and same-different auditory discrimination (Exp. 2) when hearing familiar melodies than when hearing unfamiliar melodies. This is consistent with the specific-learning hypothesis and with exemplar-style general-auditory accounts of pattern learning, although alternative explanations are possible.

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“…Hence, there are six possible pairs of instruments. The instruments in the pairs SP, SG, VP and VG have considerably different characteristics (temporal waveform, spectral content, etc), while the instruments in the pair SV have some similar characteristics and the instruments in the pair PG are closely related, as discussed in [13]. In this way, the technique can be tested under different levels of difficulty.…”

Section: Feature Selection and Extractionmentioning

confidence: 99%

Instrument identification in polyphonic music signals based on individual partials

Barbedo¹,

Tzanetakis

2010

2010 IEEE International Conference on Acoustics, Speech and Signal Processing

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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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Automated classification of piano-guitar notes

Cited by 16 publications

References 13 publications

Music Instrument Classification using Nontonal MFCC

Music Instrument Classification using Nontonal MFCC

The familiar-melody advantage in auditory perceptual development: Parallels between spoken language acquisition and general auditory perception

Instrument identification in polyphonic music signals based on individual partials

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