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Pattern recognition from audio signals is an active research topicencompassing audio tagging, acoustic scene classification, musicclassification, and other areas. Spectrogram and mel-frequency cepstralcoefficients (MFCC) are among the most commonly used features for audio signalanalysis and classification. Recently, deep convolutional neural networks (CNN)have been successfully used for audio classification problems usingspectrogram-based 2D features. In this paper, we present SpectNet, anintegrated front-end layer that extracts spectrogram features within a CNNarchitecture that can be used for audio pattern recognition tasks. Thefront-end layer utilizes learnable gammatone filters that are initialized usingmel-scale filters. The proposed layer outputs a 2D spectrogram image which canbe fed into a 2D CNN for classification. The parameters of the entire network,including the front-end filterbank, can be updated via back-propagation. Thistraining scheme allows for fine-tuning the spectrogram-image features accordingto the target audio dataset. The proposed method is evaluated in two differentaudio signal classification tasks: heart sound anomaly detection and acousticscene classification. The proposed method shows a significant 1.02\%improvement in MACC for the heart sound classification task and 2.11\%improvement in accuracy for the acoustic scene classification task compared tothe classical spectrogram image features. The source code of our experimentscan be found at \url{https://github.com/mHealthBuet/SpectNet}

SpectNet : End-to-End Audio Signal Classification Using Learnable Spectrograms