Analyzing the Potential of Pre-Trained Embeddings for Audio Classification Tasks
| Autor: | Estefanía Cano, Sascha Grollmisch, Christian Kehling, Michael Taenzer |
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| Rok vydání: | 2021 |
| Předmět: |
Training set
Computer science business.industry Deep learning Feature extraction 020206 networking & telecommunications Context (language use) Pattern recognition 02 engineering and technology Support vector machine Classifier (linguistics) 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Domain knowledge Music information retrieval 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | EUSIPCO |
| DOI: | 10.23919/eusipco47968.2020.9287743 |
| Popis: | In the context of deep learning, the availability of large amounts of training data can play a critical role in a model’s performance. Recently, several models for audio classification have been pre-trained in a supervised or self-supervised fashion on large datasets to learn complex feature representations, socalled embeddings. These embeddings can then be extracted from smaller datasets and used to train subsequent classifiers. In the field of audio event detection (AED) for example, classifiers using these features have achieved high accuracy without the need of additional domain knowledge. This paper evaluates three state-of-the-art embeddings on six audio classification tasks from the fields of music information retrieval and industrial sound analysis. The embeddings are systematically evaluated by analyzing the influence on classification accuracy of classifier architecture, fusion methods for file-wise predictions, amount of training data, and initial training domain of the embeddings. To better understand the impact of the pre-training step, results are also compared with those acquired with models trained from scratch. On average, the OpenL3 embeddings performed best with a linear SVM classifier. For a reduced amount of training examples, OpenL3 outperforms the initial baseline. |
| Databáze: | OpenAIRE |
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