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- W4210642487 abstract "Sound Event Detection (SED) is used to recognize the sound events happening in our surroundings. Recently, SED algorithms have been developed for strongly labelled data (onset-and-offset times of sound events are already labelled). However, most of the real-world sound events are weakly labelled in nature, where no offset or onset information is available. For SED, short-time Fourier transform (STFT) is generally used for obtaining time-Frequency segment (TFS) mask, which is later used for generating mel-spectrogram. Due to fixed window size used in STFT computation overall accuracy of SED degrades. Therefore, in this paper, concept of wavelet transform is explored for obtaining an accurate TFS. To enhance the classification accuracy of sound events a improved pooling technique using Gamma-tone frequency cepstral coefficients is also designed in this paper." @default.
- W4210642487 created "2022-02-08" @default.
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- W4210642487 date "2021-11-18" @default.
- W4210642487 modified "2023-09-23" @default.
- W4210642487 title "Sound Event Detection: A Wavelet Based Approach For Weakly Labelled Data" @default.
- W4210642487 doi "https://doi.org/10.1109/ibssc53889.2021.9673263" @default.
- W4210642487 hasPublicationYear "2021" @default.
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