· 2015
Environmental sound classification with convolutional neural networks
- 920citations
- 2015year
Short summary
Convolutional neural networks (CNNs) achieve state-of-the-art performance in classifying environmental sounds using segmented spectrograms with deltas.
AI-generated from the title and abstract; the full text is not read.
Key points
- A deep CNN model was developed for environmental sound classification.
- The model utilizes segmented spectrograms with deltas as input features.
- CNNs outperformed baseline methods using mel-frequency cepstral coefficients.
- The approach achieved results comparable to existing state-of-the-art methods on public datasets.
AI-generated from the title and abstract; the full text is not read.
Abstract
This paper evaluates the potential of convolutional neural networks in classifying short audio clips of environmental sounds. A deep model consisting of 2 convolutional layers with max-pooling and 2 fully connected layers is trained on a low level representation of audio data (segmented spectrograms) with deltas. The accuracy of the network is evaluated on 3 public datasets of environmental and urban recordings. The model outperforms baseline implementations relying on mel-frequency cepstral coefficients and achieves results comparable to other state-of-the-art approaches.
The authors' abstract, as published at the source., 2015 · DOI ↗
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Field: Signal Processing
Signal ProcessingComputer Science