Music Instrument Classification using Nontonal MFCC

Wu, Yi; Wang, Qi; Liu, Ruolun

doi:10.2991/fmsmt-17.2017.88

Cited by 2 publications

(1 citation statement)

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“…The utility of Mel-Frequency Cepstral Coefficients (MFCCs) in speech recognition systems has been long demonstrated [17]. Through provision of a "compact representation of the spectral envelope" [18], MFCCs have proved similarly well suited towards applications in computational musicology, such as genre and artistidentification [19], [20], and violin bow stroke classification [21]. Zheng, Zhang and Song [22] define MFCCs as "the results of a cosine transform of the real logarithm of the STFT expressed on a mel-frequency scale"; a scale noted by Stevens [23] to approximate human auditory perception.…”

Section: B Audio Feature Extraction Techniquesmentioning

confidence: 99%

Ubiquitous Multimodality as a Tool in Violin Performance Classification

Wilson,

Granieri,

Ali-MacLachlan

2023

2023 4th International Symposium on the Internet of Sounds

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Through integrated sensors, wearable devices such as fitness trackers and smart-watches provide convenient interfaces by which multimodal time-series data may be recorded. Fostering multimodality in data collection allows for the observation of recorded actions, exercises or performances with consideration towards multiple transpiring aspects. This paper details an exploration of machine-learning based classification upon a dataset of audio-gestural violin recordings, collated through the use of a purpose-built smartwatch application. This interface allowed for the recording of synchronous gestural and audio data, which proved well-suited towards classification by deep neural networks (DNNs). Recordings were segmented into individual bow strokes, these were classified through completion of three tasks: Participant Recognition, Articulation Recognition, and Scale Recognition. Higher participant classification accuracies were observed through the use of lone gestural data, while multi-input deep neural networks (MI-DNNs) achieved varying increases in accuracy during completion of the latter two tasks, through concatenation of separate audio and gestural subnetworks. Across tasks and across network architectures, testclassification accuracies ranged between 63.83% and 99.67%. Articulation Recognition accuracies were consistently high, averaging 99.37%.

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Section: B Audio Feature Extraction Techniquesmentioning

confidence: 99%

Ubiquitous Multimodality as a Tool in Violin Performance Classification

Wilson,

Granieri,

Ali-MacLachlan

2023

2023 4th International Symposium on the Internet of Sounds

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Individual Violin Recognition Method Combining Tonal and Nontonal Features

Wang

Bao

2020

Electronics

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Individual recognition among instruments of the same type is a challenging problem and it has been rarely investigated. In this study, the individual recognition of violins is explored. Based on the source–filter model, the spectrum can be divided into tonal content and nontonal content, which reflects the timbre from complementary aspects. The tonal/nontonal gammatone frequency cepstral coefficients (GFCC) are combined to describe the corresponding spectrum contents in this study. In the recognition system, Gaussian mixture models–universal background model (GMM–UBM) is employed to parameterize the distribution of the combined features. In order to evaluate the recognition task of violin individuals, a solo dataset including 86 violins is developed in this study. Compared with other features, the combined features show a better performance in both individual violin recognition and violin grade classification. Experimental results also show the GMM–UBM outperforms the CNN, especially when the training data are limited. Finally, the effect of players on the individual violin recognition is investigated.

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Music Instrument Classification using Nontonal MFCC

Cited by 2 publications

References 10 publications

Ubiquitous Multimodality as a Tool in Violin Performance Classification

Ubiquitous Multimodality as a Tool in Violin Performance Classification

Individual Violin Recognition Method Combining Tonal and Nontonal Features

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