• Corpus ID: 86790185

Audio representation for environmental sound classification using convolutional neural networks

@inproceedings{Lexfors2018AudioRF,
  title={Audio representation for environmental sound classification using convolutional neural networks},
  author={Linus Lexfors and Malte Johansson},
  year={2018}
}
A convolutional neural network (CNN) training framework is described and implemented. The framework is used to train and evaluate an audio classification system, focused on evaluating differences in audio representation. The dataset used is ESC-50, containing 50 different classes of audio. We used SBCNN, a promising architecture suited for embedded systems because of its relatively small size. Several models are trained and evaluated. Linear spectrograms versus mel-scaled spectrograms are… 

References

SHOWING 1-10 OF 19 REFERENCES
Comparison of Time-Frequency Representations for Environmental Sound Classification using Convolutional Neural Networks
TLDR
This study supports the hypothesis that time-frequency representations are valuable in learning useful features for sound classification and observes that the optimal window size during transformation is dependent on the characteristics of the audio signal and architecturally, 2D convolution yielded better results in most cases compared to 1D.
Environmental sound classification with convolutional neural networks
  • Karol J. Piczak
  • Computer Science
    2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP)
  • 2015
TLDR
The model outperforms baseline implementations relying on mel-frequency cepstral coefficients and achieves results comparable to other state-of-the-art approaches.
Unsupervised Filterbank Learning Using Convolutional Restricted Boltzmann Machine for Environmental Sound Classification
TLDR
Using CNN classifier, the ConvRBM filterbank and its score-level fusion with the Mel filterbank energies (FBEs) gave an absolute improvement of 10.65 %, and 18.70 % in the classification accuracy, respectively, over FBEs alone on the ESC-50 database, shows that the proposed ConvR BM filterbank also contains highly complementary information over the Mel filters, which is helpful in the ESC task.
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification
TLDR
It is shown that the improved performance stems from the combination of a deep, high-capacity model and an augmented training set: this combination outperforms both the proposed CNN without augmentation and a “shallow” dictionary learning model with augmentation.
Environmental Sound Classification Based on Multi-temporal Resolution CNN Network Combining with Multi-level Features
TLDR
Results demonstrate that the proposed method is highly effective in the classification tasks by employing multi-temporal resolution and multi-level features, and it outperforms the previous methods which only account for single- level features.
ImageNet classification with deep convolutional neural networks
TLDR
A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
ESC: Dataset for Environmental Sound Classification
TLDR
A new annotated collection of 2000 short clips comprising 50 classes of various common sound events, and an abundant unified compilation of 250000 unlabeled auditory excerpts extracted from recordings available through the Freesound project are presented.
Deep Learning
TLDR
Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Automatic Environmental Sound Recognition: Performance Versus Computational Cost
TLDR
Results suggest that Deep Neural Networks yield the best ratio of sound classification accuracy across a range of computational costs, while Gaussian Mixture Models offer a reasonable accuracy at a consistently small cost, and Support Vector Machines stand between both in terms of compromise between accuracy and computational cost.
Improving neural networks by preventing co-adaptation of feature detectors
When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This "overfitting" is greatly reduced by randomly omitting half of the
...
1
2
...