Aleksandr Diment

Publications

2017

• A. Diment and T. Virtanen. Transfer Learning of Weakly Labelled Audio. In proc. of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2017.
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  Many machine learning tasks have been shown solvable with impressive levels of success given large amounts of training data and computational power. For the tasks which lack data sufficient to achieve high performance, methods for transfer learning can be applied. These refer to performing the new task while having some prior knowledge of the nature of the data, gained by first performing a different task, for which training data is abundant. Previously shown successful for machine vision and natural language processing, transfer learning is investigated in this work for audio analysis. We propose to solve the problem of weak label classification (tagging) with small amounts of training data by transferring the abstract knowledge about the nature of audio data from another tagging task. Three neural network architectures are proposed and evaluated, showing impressive classification accuracy gains.

  @INPROCEEDINGS{Diment17_TL,
    author={Diment, Aleksandr and Virtanen, Tuomas},
    booktitle={Applications of Signal Processing to Audio and Acoustics (WASPAA), 2017 IEEE Workshop on},
    title={Transfer Learning of Weakly Labelled Audio},
    year={2017},
    month={Oct},
    keywords={transfer learning, tagging, weak labels, audio}
  

• J. Nikunen, A. Diment, T. Virtanen. Separation of Moving Sound Sources Using Multichannel NMF and Acoustic Tracking, accepted for publication in the IEEE Transactions on Audio, Speech and Language Processing, 2017.
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  In this paper we propose a method for separation of moving sound sources. The method is based on first tracking the sources and then estimation of source spectrograms using multichannel non-negative matrix factorization (NMF) and extracting the sources from the mixture by single-channel Wiener filtering. We propose a novel multichannel NMF model with time-varying mixing of the sources denoted by spatial covariance matrices (SCM) and provide update equations for optimizing model parameters minimizing squared Frobenius norm. The SCMs of the model are obtained based on estimated directions of arrival of tracked sources at each time frame. The evaluation is based on established objective separation criteria and using real recordings of two and three simultaneous moving sound sources. The compared methods include conventional beamforming and ideal ratio mask separation. The proposed method is shown to exceed the separation quality of other evaluated blind approaches according to all measured quantities. Additionally, we evaluate the method's susceptibility towards tracking errors by comparing the separation quality achieved using annotated ground truth source trajectories.

  @ARTICLE{2017arXiv171010005N,
     author = {{Nikunen}, J. and {Diment}, A. and {Virtanen}, T.},
      title = "{Separation of Moving Sound Sources Using Multichannel NMF and Acoustic Tracking}",
    journal = {ArXiv e-prints},
  archivePrefix = "arXiv",
     eprint = {1710.10005},
   primaryClass = "cs.SD",
   keywords = {Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
       year = 2017,
      month = oct,
     adsurl = {http://adsabs.harvard.edu/abs/2017arXiv171010005N},
    adsnote = {Provided by the SAO/NASA Astrophysics Data System}
  }
  

• T. Virtanen, A. Mesaros, T. Heittola, A. Diment, E. Vincent, E. Benetos, B. M. Elizalde. (eds.) Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017). Tampere University of Technology. Laboratory of Signal Processing, 2017. 142 p.
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  @book{DCASE17_book_bib,
  	title = "Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017)",
  	author = "Tuomas Virtanen and Annamaria Mesaros and Toni Heittola and Aleksandr Diment and Emmanuel Vincent and Emmanouil Benetos and Elizalde, {Benjamin Martinez}",
  	year = "2017",
  	month = "11",
  	publisher = "Tampere University of Technology. Laboratory of Signal Processing"
  }
  

• A. Mesaros, T. Heittola, A. Diment, B. Elizalde, A. Shah, R. Badlani, E. Vincent, B. Raj, and T. Virtanen. DCASE 2017 challenge setup: tasks, datasets and baseline system. In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017). November 2017.
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  DCASE 2017 Challenge consists of four tasks: acoustic scene classification, detection of rare sound events, sound event detection in real-life audio, and large-scale weakly supervised sound event detection for smart cars. This paper presents the setup of these tasks: task definition, dataset, experimental setup, and baseline system results on the development dataset. The baseline systems for all tasks rely on the same implementation using multilayer perceptron and log mel-energies, but differ in the structure of the output layer and the decision making process, as well as the evaluation of system output using task specific metrics.

  @inproceedings{DCASE2017challenge,
      Author = "Mesaros, A. and Heittola, T. and Diment, A. and Elizalde, B. and Shah, A. and Badlani, R. and Vincent, E. and Raj, B. and Virtanen, T.",
      title = "{DCASE} 2017 Challenge Setup: Tasks, Datasets and Baseline System",
      booktitle = "Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017)",
      month = "November",
      year = "2017",
      keywords = "Sound scene analysis, Acoustic scene classification, Sound event detection, Audio tagging, Rare sound events"
  }
  

• M. Valenti, A. Diment, G. Parascandolo, S. Squartini, T. Virtanen. DCASE 2016 Acoustic Scene Classification Using Convolutional Neural Networks. In proceedings of the 2017 International Joint Conference on Neural Networks, Anchorage, Alaska, USA.
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  This paper presents a novel application of convo- lutional neural networks (CNNs) for the task of acoustic scene classification (ASC). We here propose the use of a CNN trained to classify short sequences of audio, represented by their log-mel spectrogram. We also introduce a training method that can be used under particular circumstances in order to make full use of small datasets. The proposed system is tested and evaluated on three different ASC datasets and compared to other state-of-the- art systems which competed in the “Detection and Classification of Acoustic Scenes and Events” (DCASE) challenges held in 2016 and 2013. The best accuracy scores obtained by our system on the DCASE 2016 datasets are 79.0% (development) and 86.2% (evaluation), which constitute a 6.4% and 9% improvements with respect to the baseline system. Finally, when tested on the DCASE 2013 evaluation dataset, the proposed system manages to reach a 77.0% accuracy, improving by 1% the challenge winner’s score.

  @INPROCEEDINGS{Valenti17,
    AUTHOR="Michele Valenti and Aleksandr Diment and Giambattista Parascandolo and Stefano Squartini and Tuomas Virtanen",
    TITLE="{DCASE} 2016 Acoustic Scene Classification Using Convolutional Neural Networks",
    BOOKTITLE="Proc. of the 2017 International Joint Conference on Neural Networks",
    ADDRESS="Anchorage, Alaska, USA",
    DAYS=14-19,
    MONTH=may,
    YEAR=2017,
  }
  

2016

• M. Valenti, A. Diment, G. Parascandolo, S. Squartini, T. Virtanen. DCASE 2016 Acoustic Scene Classification Using Convolutional Neural Networks. In proceedings of the 2016 Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2016), Budapest, Hungary.
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  This workshop paper presents our contribution for the task of acoustic scene classification proposed for the "detection and classification of acoustic scenes and events" (D-CASE) 2016 challenge. We propose the use of a convolutional neural network trained to classify short sequences of audio, represented by their log-mel spectrogram. In addition we use a training method that can be used when the validation performance of the system saturates as the training proceeds. The performance is evaluated on the public acoustic scene classification development dataset provided for the D-CASE challenge. The best accuracy score obtained by our configuration on a four-folded cross-validation setup is 79.0%. It constitutes a 8.8% relative improvement with respect to the baseline system, based on a Gaussian mixture model classifier.

  @INPROCEEDINGS{Valenti16_DCASE,
  AUTHOR="Michele Valenti and Aleksandr Diment and Giambattista Parascandolo and Stefano Squartini and Tuomas Virtanen",
  TITLE="{DCASE} 2016 Acoustic Scene Classification Using Convolutional Neural Networks",
  BOOKTITLE="Proc. of the 2016 Workshop on Detection and Classification of Acoustic Scenes and Events",
  ADDRESS="Budapest, Hungary",
  DAYS=3,
  MONTH=sep,
  YEAR=2016,
  KEYWORDS="Acoustic scene classification, convolutional neural networks, DCASE, computational audio processing"
  }
  

• A. Diment, M. Parviainen, T. Virtanen, R. Zelov, A. Glasman. Noise-robust detection of whispering in telephone calls using deep neural networks. First published in the Proceedings of the 24th European Signal Processing Conference (EUSIPCO-2016) in 2016, published by EURASIP.
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  Detection of whispered speech in the presence of high levels of background noise has applications in fraudulent behaviour recognition. For instance, it can serve as an indicator of possible insider trading. We propose a deep neural network (DNN) -based whispering detection system, which operates on both magnitude and phase features, including the group delay feature from all-pole models (APGD). We show that the APGD feature outperforms the conventional ones. Trained and evaluated on the collected diverse dataset of whispered and normal speech with emulated phone line distortions and significant amounts of added background noise, the proposed system performs with accuracies as high as 91.8%.

  @INPROCEEDINGS{Diment16_WHI,
  AUTHOR="Aleksandr Diment and Tuomas Virtanen and Mikko Parviainen and Roman Zelov
  and Alex Glasman",
  TITLE="{Noise-Robust} Detection of Whispering in Telephone Calls Using Deep Neural
  Networks",
  BOOKTITLE="2016 24th European Signal Processing Conference (EUSIPCO) (EUSIPCO 2016)",
  ADDRESS="Budapest, Hungary",
  DAYS=27,
  MONTH=aug,
  YEAR=2016,
  KEYWORDS="whispering, noise robustness, deep neural networks"
  }
  

Thesis supervision

• V.-V. Eklund. Audio dataset creation, BSc thesis, Tampere University of Technology, 2018.
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The goal of this thesis was to build an easily accessible and approachable environmental audio dataset for benchmarking machine learning algorithms. The samples in the created dataset were collected from Freesound, which is an online database consisting of sounds uploaded by its users. The dataset consists of three classes, each containing 50 instances of original recordings with annotations for 5-second segments. The classes are 'dog', 'bird' and 'rain', and the dataset was therefore given the name DBR dataset. Additionally, a script for evaluating support vector, random forest and k-NN classifers with the dataset was written. The building and evaluation stages were documented step-by-step and a Jupyter notebook tutorial for using the dataset was created. The dataset, the scripts and the notebook were published online.

@thesis{Eklund18_BSc,
  author = {Ville-Veikko Eklund},
  title = {Audio dataset creation},
  school = {Tampere University of Technology},
  address = {Finland},
  year = {2018},
  type={Bachelor's Thesis}
  }

• M. Valenti. Convolutional neural networks for acoustic scene classification, MSc thesis, Tampere University of Technology, 2016.
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  In this thesis we investigate the use of deep neural networks applied to the field of computational audio scene analysis, in particular to acoustic scene classification. This task concerns the recognition of an acoustic scene, like a park or a home, performed by an artificial system. In our work we examine the use of deep models aiming to give a contribution in one of their use cases which is, in our opinion, one of the most poorly explored. The neural architecture we propose in this work is a convolutional neural network specifically designed to work on a time-frequency audio representation known as log-mel spectrogram. The network output is an array of prediction scores, each of which is associated with one class of a set of 15 predefined classes. In addition, the architecture features batch normalization, a recently proposed regularization technique used to enhance the network performance and to speed up its training.

  We also investigate the use of different audio sequence lengths as classification unit for our network. Thanks to these experiments we observe that, for our artificial system, the recognition of long sequences is not easier than of medium-length sequences, hence highlighting a counterintuitive behaviour. Moreover, we introduce a training procedure which aims to make the best of small datasets by using all the labeled data available for the network training. This procedure, possible under particular circumstances, constitutes a trade-off between an accurate training stop and an increased data representation available to the network. Finally, we compare our model to other systems, proving that its recognition ability can outperform either other neural architectures as well as other state-of-the-art statistical classifiers, like support vector machines and Gaussian mixture models. The proposed system reaches good accuracy scores on two different databases collected in 2013 and 2016. The best accuracy scores, obtained according to two cross-validation setups, are 77% and 79% respectively. These scores constitute a 22% and 6.1% accuracy increment with respect to the correspondent baselines published together with datasets.

  @MastersThesis{Valenti16_MSc_bib,
    author = {Michele Valenti},
    title = {Convolutional Neural Networks for Acoustic Scene Classification},
    school = {Tampere University of Technology},
    address = {Finland},
    year = {2016},
    }

Teaching

• 2016-2017:
  • Advanced Signal Processing Laboratory.
• 2014-2015:
  • Audio and Speech Processing.
• 2013-2014:
  • Audio and Speech Processing.
  • Analysis of Audio, Speech and Music Signals.
• 2012-2013:
  • Speech recognition.

A collection of various LaTeX templates, MATLAB tricks etc., as well as totally unrelated stuff.

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