Learning Sparse Adversarial Dictionaries For Multi-Class Audio Classification
| Autor: | Puranjoy Bhattacharya, Vaisakh Shaj |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Computer science business.industry Noise reduction Speech recognition 02 engineering and technology 010501 environmental sciences 01 natural sciences Class (biology) Machine Learning (cs.LG) Term (time) Speech enhancement Noise Computer Science - Learning Discriminative model Computer Science::Sound 0202 electrical engineering electronic engineering information engineering Task analysis 020201 artificial intelligence & image processing Artificial intelligence business 0105 earth and related environmental sciences Sparse matrix |
| Zdroj: | ACPR |
| Popis: | Audio events are quite often overlapping in nature, and more prone to noise than visual signals. There has been increasing evidence for the superior performance of representations learned using sparse dictionaries for applications like audio denoising and speech enhancement. This paper concentrates on modifying the traditional reconstructive dictionary learning algorithms, by incorporating a discriminative term into the objective function in order to learn class-specific adversarial dictionaries that are good at representing samples of their own class at the same time poor at representing samples belonging to any other class. We quantitatively demonstrate the effectiveness of our learned dictionaries as a stand-alone solution for both binary as well as multi-class audio classification problems. Accepted in Asian Conference of Pattern Recognition (ACPR-2017) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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