Proceedings ArticleDOI
Acoustic Scene Classification in Hearing aid using Deep Learning
VS Vivek,S Vidhya,P Madhanmohan +2 more
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TLDR
A simple sound classification system that could be used to automatically switch between various hearing aid algorithms based on the auditory related scene and accomplishes high precision with just three to five second duration per scene.Abstract:
Different audio environments require different settings in hearing aid to acquire high-quality speech. Manual tuning of hearing aid settings can be irritating. Thus, hearing aids can be provided with options and settings that can be tuned based on the audio environment. In this paper we provide a simple sound classification system that could be used to automatically switch between various hearing aid algorithms based on the auditory related scene. Features like MFCC, Mel-spectrogram, Chroma, Spectral contrast and Tonnetz are extracted from several hours of audio from five classes like “music,” “noise,” “speech with noise,” “silence,” and “clean speech” for training and testing the network. Using these features audio is processed by the convolution neural network. We show that our system accomplishes high precision with just three to five second duration per scene. The algorithm is efficient and consumes less memory footprint. It is possible to implement the system in digital hearing aid.read more
Citations
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Journal ArticleDOI
Deep Learning in Diverse Intelligent Sensor Based Systems
TL;DR: Deep learning has become a predominant method for solving data analysis problems in virtually all fields of science and engineering as mentioned in this paper , and the increasing complexity and large volume of data collected by diverse sensor systems have spurred the development of deep learning methods and have fundamentally transformed the way the data are acquired, processed, analyzed, and interpreted.
Book ChapterDOI
Late Fusion of Convolutional Neural Network with Wavelet-Based Ensemble Classifier for Acoustic Scene Classification
Cheng Siong Chin,Jianhua Zhang +1 more
TL;DR: In this paper, the authors proposed a late fusion of multi-model using CNN and ensemble classifiers, which achieved an accuracy of 79.43% in the TUT 2017 Challenge-Task 1.
Journal ArticleDOI
New Avenues in Audio Intelligence: Towards Holistic Real-life Audio Understanding.
Björn Schuller,Björn Schuller,Alice Baird,Alexander Gebhard,Shahin Amiriparian,Gil Keren,Maximilian Schmitt,Nicholas Cummins,Nicholas Cummins +8 more
TL;DR: In this paper, a computer audition (i.e., intelligent audio) has made great strides in recent years; however, it is still far from achieving holistic hearing abilities, which more appropriately mimic human-like und...
Journal ArticleDOI
Determining Ratio of Prunable Channels in MobileNet by Sparsity for Acoustic Scene Classification
TL;DR: In this article , the authors proposed a method that determines the ratio of pruned channels by simple linear regression models related to the Sparsity of Channels (S C ) in the convolutional layers.
Exploring the Effects of Channel Sparsity on Neural Network Pruning for Acoustic Scene Classification
TL;DR: In this paper , the internal weights acquired by convolutional neural networks that will undergone pruning are used to create a novel metric, Weight Skewness (WS), for quantifying the sparsity of channels.
References
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Proceedings Article
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