Semantic audio analysis has become a fundamental task in modern audio applications, making the improvement and optimization of classification algorithms a necessity. Standard frame-based audio classification methods have been optimized, and modern approaches introduce engineering methodologies that capture the temporal dependency between successive feature observations, following the process of temporal feature integration. Moreover, the deployment of the convolutional neural networks defined a new era on semantic audio analysis. This paper attempts a thorough comparison between standard feature-based classification strategies, state-of-the-art temporal feature integration tactics, and 1D/2D deep convolutional neural network setups on typical audio classification tasks. Experiments focus on optimizing a lightweight configuration for convolutional network topologies on a Speech/Music/Other classification scheme that can be deployed on various audio information retrieval tasks, such as voice activity detection, speaker diarization, or speech emotion recognition. The main target of this work is the establishment of an optimized protocol for constructing deep convolutional topologies on general audio detection classification schemes, minimizing complexity and computational needs.
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