Audio Classification of Bit-Representation Waveform
| Autor: | Masaki Okawa, Naoki Sawada, Takuya Saito, Hiromitsu Nishizaki |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Sound (cs.SD) Computer science Speech recognition 02 engineering and technology Computer Science - Sound Machine Learning (cs.LG) law.invention Raw audio format symbols.namesake Audio and Speech Processing (eess.AS) law Computer Science::Multimedia FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering Waveform Representation (mathematics) Frequency analysis Computer Science - Computation and Language Artificial neural network business.industry Deep learning Spectral density 020206 networking & telecommunications ComputingMethodologies_PATTERNRECOGNITION Fourier transform Computer Science::Sound symbols 020201 artificial intelligence & image processing Artificial intelligence business Computation and Language (cs.CL) Electrical Engineering and Systems Science - Audio and Speech Processing |
| Zdroj: | INTERSPEECH |
| DOI: | 10.21437/interspeech.2019-1855 |
| Popis: | This study investigated the waveform representation for audio signal classification. Recently, many studies on audio waveform classification such as acoustic event detection and music genre classification have been published. Most studies on audio waveform classification have proposed the use of a deep learning (neural network) framework. Generally, a frequency analysis method such as Fourier transform is applied to extract the frequency or spectral information from the input audio waveform before inputting the raw audio waveform into the neural network. In contrast to these previous studies, in this paper, we propose a novel waveform representation method, in which audio waveforms are represented as a bit sequence, for audio classification. In our experiment, we compare the proposed bit representation waveform, which is directly given to a neural network, to other representations of audio waveforms such as a raw audio waveform and a power spectrum with two classification tasks: one is an acoustic event classification task and the other is a sound/music classification task. The experimental results showed that the bit representation waveform achieved the best classification performance for both the tasks. Comment: Accepted at INTERSPEECH2019 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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