Open AccessAudio Tagging Using CNN Based Audio Neural Networks for Massive Data Processing
Author(s) -
J. Samuel Manoharan
Publication year - 2021
Publication title -
journal of artificial intelligence and copsule networks
Language(s) - English
Resource type - Journals
ISSN - 2582-2012
DOI - 10.36548/jaicn.2021.4.008
Subject(s) - computer science , spectrogram , artificial neural network , audio signal processing , speech recognition , convolutional neural network , artificial intelligence , audio mining , pattern recognition (psychology) , audio signal , acoustic model , speech processing , speech coding
Sound event detection, speech emotion classification, music classification, acoustic scene classification, audio tagging and several other audio pattern recognition applications are largely dependent on the growing machine learning technology. The audio pattern recognition issues are also addressed by neural networks in recent days. The existing systems operate within limited durations on specific datasets. Pretrained systems with large datasets in natural language processing and computer vision applications over the recent years perform well in several tasks. However, audio pattern recognition research with large-scale datasets is limited in the current scenario. In this paper, a large-scale audio dataset is used for training a pre-trained audio neural network. Several audio related tasks are performed by transferring this audio neural network. Several convolution neural networks are used for modeling the proposed audio neural network. The computational complexity and performance of this system are analyzed. The waveform and leg-mel spectrogram are used as input features in this architecture. During audio tagging, the proposed system outperforms the existing systems with a mean average of 0.45. The performance of the proposed model is demonstrated by applying the audio neural network to five specific audio pattern recognition tasks.