Explore acoustic scene classification using deep learning techniques to identify the environment of audio clips, with applications in surveillance, aiding disabled individuals, and context-aware computing. Discover related works in CNN and LSTM combinations for audio classification, addressing the gap of limited data through data augmentation methods.
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Introduction Acoustic scene classification is the process of identifying the scenario/environment of an audio clip. The goal is to classify a test recording into one of the provided predefined classes that characterizes the environment in which it was recorded for example park , home , office .
Applications 1. Surveillance 2. Hearing aid for disabled people 3. For Context aware computers
Related works Convolutional Neural Network trained to classify short sequences of audio (ref: DCASE 2016 Acoustic scene classification using convolutional neural networks by Michele Valenti et al - 2016) Combination of CNN and LSTM (ref: Acoustic scene classification using parallel combination of LSTM and CNN by Soo Hyun Bae et al - 2016) Combination of CNN and Data augmentation (ref: Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification by Justin Salamon and Juan Pablo Bello - 2016)
Gap For a particular class there is not much data like in case of images So a way can be found to create more data from the available data which is technically called as data augmentation.
