Applications of Soft Computing for Musical Instrument Classification

Piccoli, Daniel; Abernethy, Mark; Shri, M.; Khan, Shamim

doi:10.1007/978-3-540-24581-0_75

Cited by 2 publications

(2 citation statements)

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“…Besides of audio file length, there were also a variety of the samples frame size and filter techniques used before: 256 samples with hamming-windowed was used in [3], 2048 samples with hanning-windowed in [12], and 4096 samples in [13]. There were assortments of sampling rate used in the previous work instead of 44.1 kHz as well.…”

Section: A Data Representationmentioning

confidence: 99%

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Feature Extraction for Traditional Malay Musical Instruments Classification System

Senan

Ibrahim

Nawi

et al. 2009

2009 International Conference of Soft Computing and Pattern Recognition

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Automatic musical instrument classification system deals with a large number of sound database and various types of features schemes. With the lack of data preprocessing, it might become invaluable asset that can impact the whole classification tasks. In handling an effective classification system, finding the best data sets with the best features schemes often a vital step in the data representation and feature extraction process. Thus, this study is conducted in order to investigate the impact of several factors that might affecting the classification accuracy such as audio length, segmented frame size and data sets size (for training and testing) towards Traditional Malay musical instruments sounds classification system. The perception-based and MFCC features schemes with total of 37 features was utilized in this study. Meanwhile, Multi-Layered Perceptrons classifier is employed to evaluate the modified data sets and extracted features schemes in terms of their classification performance. Results show that the highest accuracy of 99.57% was obtained from the best data sets with the combination of full features. It is also revealed that the identified factors had a significant role to the performance of classification accuracy. Hence, this study suggest that further feature analysis research is necessary for better solution in Traditional Malay musical instruments sounds classification system problem.

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Section: A Data Representationmentioning

confidence: 99%

“…There were assortments of sampling rate used in the previous work instead of 44.1 kHz as well. For example, 16kHz [14], 22kHz [12] and 32kHz [15]. These variety of parameters used in the literature show that there were no standard benchmarking in determining the best parameter for data representation.…”

Section: A Data Representationmentioning

confidence: 99%