Abstract:
In this paper we study the problem of acoustic scene classification, i.e., categorization of audio sequences into mutually exclusive classes based on their spectral conte...Show MoreMetadata
Abstract:
In this paper we study the problem of acoustic scene classification, i.e., categorization of audio sequences into mutually exclusive classes based on their spectral content. We describe the methods and results discovered during a competition organized in the context of a graduate machine learning course; both by the students and external participants. We identify the most suitable methods and study the impact of each by performing an ablation study of the mixture of approaches. We also compare the results with a neural network baseline, and show the improvement over that. Finally, we discuss the impact of using a competition as a part of a university course, and justify its importance in the curriculum based on student feedback.
Published in: 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)
Date of Conference: 17-20 September 2018
Date Added to IEEE Xplore: 01 November 2018
ISBN Information:
Print on Demand(PoD) ISSN: 1551-2541
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Scene Classification ,
- Acoustic Scene ,
- Acoustic Scene Classification ,
- Neural Network ,
- Student Feedback ,
- Course Of Learning ,
- Part Of Course ,
- Convolutional Neural Network ,
- Hidden Markov Model ,
- Recurrent Neural Network ,
- Data Augmentation ,
- Background Subtraction ,
- Kullback-Leibler ,
- Rate Set ,
- Gaussian Mixture Model ,
- Domain Adaptation ,
- Semi-supervised Learning ,
- Gated Recurrent Unit ,
- Sound Detection ,
- Audio Segments ,
- Original Recordings ,
- Frequency Bins ,
- Sound Effects ,
- Examples Of Feedback ,
- Data Augmentation Techniques ,
- Mel-frequency Cepstral Coefficients ,
- Top Team ,
- Augmentation Techniques ,
- Deep Learning
- Author Keywords
Contents Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Scene Classification ,
- Acoustic Scene ,
- Acoustic Scene Classification ,
- Neural Network ,
- Student Feedback ,
- Course Of Learning ,
- Part Of Course ,
- Convolutional Neural Network ,
- Hidden Markov Model ,
- Recurrent Neural Network ,
- Data Augmentation ,
- Background Subtraction ,
- Kullback-Leibler ,
- Rate Set ,
- Gaussian Mixture Model ,
- Domain Adaptation ,
- Semi-supervised Learning ,
- Gated Recurrent Unit ,
- Sound Detection ,
- Audio Segments ,
- Original Recordings ,
- Frequency Bins ,
- Sound Effects ,
- Examples Of Feedback ,
- Data Augmentation Techniques ,
- Mel-frequency Cepstral Coefficients ,
- Top Team ,
- Augmentation Techniques ,
- Deep Learning
- Author Keywords
