Environmental Sound Recognition Using Masked Conditional Neural Networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Neural network based architectures used for sound recognition are usually adapted from other application domains, which may not harness sound related properties. The ConditionaL Neural Network (CLNN) is designed to consider the relational properties across frames in a temporal signal, and its extension the Masked ConditionaL Neural Network (MCLNN) embeds a filterbank behavior within the network, which enforces the network to learn in frequency bands rather than bins. Additionally, it automates the exploration of different feature combinations analogous to handcrafting the optimum combination of features for a recognition task. We applied the MCLNN to the environmental sounds of the ESC-10 dataset. The MCLNN achieved competitive accuracies compared to state-of-the-art convolutional neural networks and hand-crafted attempts.

Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications - 13th International Conference, ADMA 2017, Proceedings
Subtitle of host publication13th International Conference, ADMA 2017, Singapore, November 5–6, 2017, Proceedings
EditorsWen-Chih Peng, Wei Emma Zhang, Gao Cong, Aixin Sun, Chengliang Li
Number of pages13
ISBN (Electronic)978-3-319-69179-4
Publication statusPublished - 5 Nov 2017
EventAdvanced Data Mining and Applications - , Singapore
Duration: 5 Nov 2017 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10604 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceAdvanced Data Mining and Applications
Abbreviated titleADMA
Period5/11/17 → …


  • Boltzmann machine
  • CLNN
  • CRBM
  • Conditional RBM
  • Conditional neural network
  • DNN
  • Deep neural network
  • ESR
  • Environmental sound recognition
  • Masked Conditional neural network
  • RBM

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