Learning Features from Music Audio with Deep Belief Networks

By Philippe Hamel, Douglas Eck. 2010

309 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yoshua Bengio
2. Li Deng
3. Zhe Gan
4. Kyunghyun Cho
5. Dong Yu
6. Ruslan Salakhutdinov
7. Dacheng Tao
8. Trevor Darrell

Citations in the corpus

• Deep Learning: Methods and Applications (2014, background): “In the general field of audio and music processing, the impacted areas by deep learning include mainly music signal processing and music information retrieval [15, 22, 141, 177, 178, 179, 319].”
• Deep Scattering Spectrum (2013, background): “A deep belief network trained on spectrograms [18], achieves 15.”
• Temporal Pooling and Multiscale Learning for Automatic Annotation and Ranking of Music Audio (2011, background): “For instance, features learned with a Deep Belief Network over spectral amplitudes has been shown to outperform MFCCs for genre recognition and automatic annotation [10, 16].”
• Psychology Meets Machine Learning: Interdisciplinary Perspectives on Algorithmic Job Candidate Screening (2018, background): “…the more descriptive aspects of such phenomena (e.g. Collobert and Interdisciplinary Perspectives on Algorithmic Job Candidate Screening 211 Weston 2008; Hamel and Eck 2010), it is still problematic for them to capture notions of true human-like ‘understanding’ (Hofstadter 2018; Sturm 2014).”
• Improving Content-based and Hybrid Music Recommendation using Deep Learning (2014, methodology): “[7], deep belief network was used for music genre classification and autotagging, with performance surpassing that based on MFCC and MIM feature sets.”
• A survey of the recent architectures of deep convolutional neural networks (2019, unknown): “2008; Hamel and Eck 2010).”
• Deep content-based music recommendation (2013, unknown): “Recently this traditional approach has been challenged by some authors who have applied deep neural networks to MIR problems [10, 11, 12].”
• Multi-Level and Multi-Scale Feature Aggregation Using Pretrained Convolutional Neural Networks for Music Auto-Tagging (2017, background): “Hamel and Eck [6] applied deep neural networks pretrained with deep belief networks to learn hierarchical features and compared each layer of features.”
• An Analysis of the t-SNE Algorithm for Data Visualization (2018, background): “, 2009), music analysis (Hamel and Eck, 2010), cancer biology (Abdelmoula et al.”
• Visualizing the Hidden Activity of Artificial Neural Networks (2017, methodology): “5, have been used as high-level evidence of model efficacy [7, 29, 41, 36, 16].”
• Learning emotion-based acoustic features with deep belief networks (2011, background): “Hamel and Eck applied deep belief networks (DBNs) to the problems of musical genre identification and autotagging [10].”
• Deep Learning in Spiking Neural Networks (2018, methodology): “DBNs and convolutional DBNs have successfully been employed in many areas such as visual processing [192], [193], [194], [195], audio processing [196], [197], [198], [199], [200], time series forecasting [201], and protein folding [202].”
• Analyzing Drum Patterns Using Conditional Deep Belief Networks (2012, methodology): “This greedy layer-by-layer training approach is a useful procedure for learning a set of non-linear features in an unsupervised manner [4], and it has been shown to be a beneficial pre-training when followed by discriminative backpropagation [6].”
• High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning (2016, methodology): “, a DBN in combination with labelled SVMs such as binary or multiclass SVMs) have been used in several application domains, such as object classification [14], text classification [27], music classification [29], face expression recognition [41], and speech separation [42].”
• Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks (2015, background): “…produces the state-of-the-art performance on digit recognition (LeCun et al. 1989; Hin-ton, Osindero, and Teh 2006), image classification (Cires¸an et al. 2011; Krizhevsky, Sutskever, and Hinton 2012), musical signal processing (Hamel and Eck 2010) and natural language processing (Maas et al. 2011).”
• Moving Beyond Feature Design: Deep Architectures and Automatic Feature Learning in Music Informatics (2012, background): “transcription [30], genre identification [14] and mood prediction [32], out-performing other shallow, MFCC-based systems.”