Open-Set Acoustic Scene Classification with Deep Convolutional Autoencoders

Acoustic scene classification is the task of determining the environment in which a given audio file has been recorded. If it is a priori not known whether all possible environments that may be encountered during test time are also known when training the system, the task is referred to as open-set classification. This paper contains a description of an open-set acoustic scene classification system submitted to task 1C of the Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge 2019. Our system consists of a combination of convolutional neural networks for closed-set identification and deep convolutional autoencoders for outlier detection. On the evaluation dataset of the challenge, our proposed system significantly outperforms the baseline system and improves the score from 0.476 to 0.621. Moreover, our submitted system ranked 3rd among all teams in task 1C.

[1]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[2]  Hongxing He,et al.  Outlier Detection Using Replicator Neural Networks , 2002, DaWaK.

[3]  Tuomas Virtanen,et al.  A multi-device dataset for urban acoustic scene classification , 2018, DCASE.

[4]  Tuomas Virtanen,et al.  Acoustic Scene Classification: An Overview of Dcase 2017 Challenge Entries , 2018, 2018 16th International Workshop on Acoustic Signal Enhancement (IWAENC).

[5]  Annamaria Mesaros,et al.  Acoustic Scene Classification in DCASE 2019 Challenge: Closed and Open Set Classification and Data Mismatch Setups , 2019, DCASE.

[6]  Derry Fitzgerald,et al.  Harmonic/Percussive Separation Using Median Filtering , 2010 .

[7]  Gerhard Widmer,et al.  CP-JKU SUBMISSIONS FOR DCASE-2016 : A HYBRID APPROACH USING BINAURAL I-VECTORS AND DEEP CONVOLUTIONAL NEURAL NETWORKS , 2016 .

[8]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[9]  Colin Raffel,et al.  librosa: Audio and Music Signal Analysis in Python , 2015, SciPy.

[10]  Bolun Wang,et al.  CIAIC-ASC SYSTEM FOR DCASE 2019 CHALLENGE TASK1 Technical Report , 2019 .

[11]  Terrance E. Boult,et al.  Probability Models for Open Set Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[13]  Nicholas W. D. Evans,et al.  The open-set problem in acoustic scene classification , 2016, 2016 IEEE International Workshop on Acoustic Signal Enhancement (IWAENC).

[14]  Mark D. McDonnell,et al.  Acoustic Scene Classification Using Deep Residual Networks with Late Fusion of Separated High and Low Frequency Paths , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[15]  Patrick Kenny,et al.  Front-End Factor Analysis for Speaker Verification , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[16]  Justin Salamon,et al.  Look, Listen, and Learn More: Design Choices for Deep Audio Embeddings , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[17]  ScheirerWalter,et al.  Toward Open Set Recognition , 2013 .

[18]  Yong Xu,et al.  Cross-task learning for audio tagging, sound event detection and spatial localization: DCASE 2019 baseline systems , 2019, ArXiv.

[19]  Mathieu Lagrange,et al.  Detection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge , 2018, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[20]  Kyogu Lee,et al.  Convolutional Neural Networks with Binaural Representations and Background Subtraction for Acoustic Scene Classification , 2017, DCASE.

[21]  Yonghong Yan,et al.  Integrating the Data Augmentation Scheme with Various Classifiers for Acoustic Scene Modeling , 2019, ArXiv.

[22]  Graham W. Taylor,et al.  Improved Regularization of Convolutional Neural Networks with Cutout , 2017, ArXiv.

[23]  Hongyi Zhang,et al.  mixup: Beyond Empirical Risk Minimization , 2017, ICLR.

[24]  Gerhard Widmer,et al.  ACOUSTIC SCENE CLASSIFICATION WITH REJECT OPTION BASED ON RESNETS , 2019 .

[25]  Terrance E. Boult,et al.  Towards Open Set Deep Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Heikki Huttunen,et al.  ACOUSTIC SCENE CLASSIFICATION: A COMPETITION REVIEW , 2018, 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP).

[27]  Andrew Zisserman,et al.  Look, Listen and Learn , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[28]  Alexander Rakowski.  FREQUENCY-AWARE CNN FOR OPEN SET ACOUSTIC SCENE CLASSIFICATION Technical Report , 2019 .

[29]  Charu C. Aggarwal,et al.  Outlier Detection with Autoencoder Ensembles , 2017, SDM.

[30]  Lukás Burget,et al.  Convolutional Neural Networks and x-vector Embedding for DCASE2018 Acoustic Scene Classification Challenge , 2018, ArXiv.

[31]  Sanjeev Khudanpur,et al.  Deep neural network-based speaker embeddings for end-to-end speaker verification , 2016, 2016 IEEE Spoken Language Technology Workshop (SLT).