Benchmarking Audio Signal Representation Techniques for Classification with Convolutional Neural Networks

Cqt-based Convolutional Neural Networks for Audio Scene Classification

In this paper, we propose a parallel Convolutional Neural Network architecture for the task of classifying acoustic scenes and urban sound scapes. A popular choice for input to a Convolutional Neural Network in audio classification problems are Mel-transformed spectrograms. We, however, show in this paper that a ConstantQ-transformed input improves results. Furthermore, we evaluated critical pa...

Unsupervised and Neural Hybrid Techniques for Audio Signal Classification

Cqt-based Convolutional Neural Networks for Audio Scene Classification and Domestic Audio Tagging

For the DCASE 2016 challenge on detection and classification of acoustic scenes and events we submitted a parallel Convolutional Neural Network architecture for the tasks of classifying acoustic scenes and urban sound scapes (task 1) and domestic audio tagging (task 4). A popular choice for input to a Convolutional Neural Network in audio classification problems are Mel-transformed spectrograms...

Audio Spectrogram Representations for Processing with Convolutional Neural Networks

One of the decisions that arise when designing a neural network for any application is how the data should be represented in order to be presented to, and possibly generated by, a neural network. For audio, the choice is less obvious than it seems to be for visual images, and a variety of representations have been used for different applications including the raw digitized sample stream, hand-c...

Scene Classification with Deep Convolutional Neural Networks

The use of massive datasets like ImageNet and the revival of Convolutional Neural Networks (CNNs) for learning deep features has significantly improved the performance of object recognition. However, performance at scene classification has not achieved the same level of success since there is still semantic gap between the deep features and the high-level context. In this project we proposed a ...