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Audio Classification

5 papers with code · Audio

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CNN Architectures for Large-Scale Audio Classification

29 Sep 2016IBM/MAX-Audio-Classifier

Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 video-level labels.

AUDIO CLASSIFICATION

Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Data

18 Feb 2016cruvadom/Convolutional-RNN

Traditional convolutional layers extract features from patches of data by applying a non-linearity on an affine function of the input. Using our convolutional recurrent layers we obtain an improvement in performance in two audio classification tasks, compared to traditional convolutional layers.

AUDIO CLASSIFICATION

A Closer Look at Weak Label Learning for Audio Events

24 Apr 2018ankitshah009/WALNet-Weak_Label_Analysis

In this work, we first describe a CNN based approach for weakly supervised training of audio events. We then describe important characteristics, which naturally arise in weakly supervised learning of sound events.

AUDIO CLASSIFICATION SOUND EVENT DETECTION

Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals

9 Jul 2018soerenab/AudioMNIST

Interpretability of deep neural networks is a recently emerging area of machine learning research targeting a better understanding of how models perform feature selection and derive their classification decisions. In this paper, two neural network architectures are trained on spectrogram and raw waveform data for audio classification tasks on a newly created audio dataset and layer-wise relevance propagation (LRP), a previously proposed interpretability method, is applied to investigate the models' feature selection and decision making.

AUDIO CLASSIFICATION DECISION MAKING

A Deep Bag-of-Features Model for Music Auto-Tagging

20 Aug 2015juhannam/deepbof

Feature learning and deep learning have drawn great attention in recent years as a way of transforming input data into more effective representations using learning algorithms. Such interest has grown in the area of music information retrieval (MIR) as well, particularly in music audio classification tasks such as auto-tagging.

AUDIO CLASSIFICATION MUSIC AUTO-TAGGING MUSIC INFORMATION RETRIEVAL