Data-Efficient Weakly Supervised Learning for Low-Resource Audio Event Detection Using Deep Learning

5 pages, 2 figures. arXiv admin note: substantial text overlap with arXiv:1807.03697We propose a method to perform audio event detection under the common constraint that only limited training data are available. In training a deep learning system to perform audio event detection, two practical problems arise. Firstly, most datasets are "weakly labelled" having only a list of events present in each recording without any temporal information for training. Secondly, deep neural networks need a very large amount of labelled training data to achieve good quality performance, yet in practice it is difficult to collect enough samples for most classes of interest. In this paper, we propose a data-efficient training of a stacked convolutional and recurrent neural network. This neural network is trained in a multi instance learning setting for which we introduce a new loss function that leads to improved training compared to the usual approaches for weakly supervised learning. We successfully test our approach on two low-resource datasets that lack temporal labels

Knowledge Transfer from Weakly Labeled Audio using Convolutional Neural Network for Sound Events and Scenes

In this work we propose approaches to effectively transfer knowledge from weakly labeled web audio data. We first describe a convolutional neural network (CNN) based framework for sound event detection and classification using weakly labeled audio data. Our model trains efficiently from audios of variable lengths; hence, it is well suited for transfer learning. We then propose methods to learn representations using this model which can be effectively used for solving the target task. We study both transductive and inductive transfer learning tasks, showing the effectiveness of our methods for both domain and task adaptation. We show that the learned representations using the proposed CNN model generalizes well enough to reach human level accuracy on ESC-50 sound events dataset and set state of art results on this dataset. We further use them for acoustic scene classification task and once again show that our proposed approaches suit well for this task as well. We also show that our methods are helpful in capturing semantic meanings and relations as well. Moreover, in this process we also set state-of-art results on Audioset dataset, relying on balanced training set.Comment: ICASSP 201

Deep Learning for Audio Signal Processing

Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross-fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e. audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.Comment: 15 pages, 2 pdf figure

음향 이벤트 탐지를 위한 효율적 데이터 활용 및 약한 교사학습 기법

학위논문(박사)--서울대학교 대학원 :공과대학 전기·컴퓨터공학부,2020. 2. 김남수.Conventional audio event detection (AED) models are based on supervised approaches. For supervised approaches, strongly labeled data is required. However, collecting large-scale strongly labeled data of audio events is challenging due to the diversity of audio event types and labeling difficulties. In this thesis, we propose data-efficient and weakly supervised techniques for AED. In the first approach, a data-efficient AED system is proposed. In the proposed system, data augmentation is performed to deal with the data sparsity problem and generate polyphonic event examples. An exemplar-based noise reduction algorithm is proposed for feature enhancement. For polyphonic event detection, a multi-labeled deep neural network (DNN) classifier is employed. An adaptive thresholding algorithm is applied as a post-processing method for robust event detection in noisy conditions. From the experimental results, the proposed algorithm has shown promising performance for AED on a low-resource dataset. In the second approach, a convolutional neural network (CNN)-based audio tagging system is proposed. The proposed model consists of a local detector and a global classifier. The local detector detects local audio words that contain distinct characteristics of events, and the global classifier summarizes the information to predict audio events on the recording. From the experimental results, we have found that the proposed model outperforms conventional artificial neural network models. In the final approach, we propose a weakly supervised AED model. The proposed model takes advantage of strengthening feature propagation from DenseNet and modeling channel-wise relationships by SENet. Also, the correlations among segments in audio recordings are represented by a recurrent neural network (RNN) and conditional random field (CRF). RNN utilizes contextual information and CRF post-processing helps to refine segment-level predictions. We evaluate our proposed method and compare its performance with a CNN based baseline approach. From a number of experiments, it has been shown that the proposed method is effective both on audio tagging and weakly supervised AED.일반적인 음향 이벤트 탐지 시스템은 교사학습을 통해 훈련된다. 교사학습을 위해서는 강한 레이블 데이터가 요구된다. 하지만 강한 레이블 데이터는 음향 이벤트의 다양성 및 레이블의 난이도로 인해 큰 데이터베이스를 구축하기 어렵다는 문제가 있다. 본 논문에서는 이러한 문제를 해결하기 위해 음향 이벤트 탐지를 위한 데이터 효율적 활용 및 약한 교사학습 기법에 대해 제안한다. 첫 번째 접근법으로서, 데이터 효율적인 음향 이벤트 탐지 시스템을 제안한다. 제안된 시스템에서는 데이터 증대 기법을 사용해 데이터 희소성 문제에 대응하고 중첩 이벤트 데이터를 생성하였다. 특징 벡터 향상을 위해 잡음 억제 기법이 사용되었고 중첩 음향 이벤트 탐지를 위해 다중 레이블 심층 인공신경망(DNN) 분류기가 사용되었다. 실험 결과, 제안된 알고리즘은 불충분한 데이터에서도 우수한 음향 이벤트 탐지 성능을 나타내었다. 두 번째 접근법으로서, 컨볼루션 신경망(CNN) 기반 오디오 태깅 시스템을 제안한다. 제안된 모델은 로컬 검출기와 글로벌 분류기로 구성된다. 로컬 검출기는 고유한 음향 이벤트 특성을 포함하는 로컬 오디오 단어를 감지하고 글로벌 분류기는 탐지된 정보를 요약하여 오디오 이벤트를 예측한다. 실험 결과, 제안된 모델이 기존 인공신경망 기법보다 우수한 성능을 나타내었다. 마지막 접근법으로서, 약한 교사학습 음향 이벤트 탐지 모델을 제안한다. 제안된 모델은 DenseNet의 구조를 활용하여 정보의 원활한 흐름을 가능하게 하고 SENet을 활용해 채널간의 상관관계를 모델링 한다. 또한, 오디오 신호에서 부분 간의 상관관계 정보를 재순환 신경망(RNN) 및 조건부 무작위 필드(CRF)를 사용해 활용하였다. 여러 실험을 통해 제안된 모델이 기존 CNN 기반 기법보다 오디오 태깅 및 음향 이벤트 탐지 모두에서 더 나은 성능을 나타냄을 보였다.1 Introduction 1 2 Audio Event Detection 5 2.1 Data-Ecient Audio Event Detection 6 2.2 Audio Tagging 7 2.3 Weakly Supervised Audio Event Detection 9 2.4 Metrics 10 3 Data-Ecient Techniques for Audio Event Detection 17 3.1 Introduction 17 3.2 DNN-Based AED system 18 3.2.1 Data Augmentation 20 3.2.2 Exemplar-Based Approach for Noise Reduction 21 3.2.3 DNN Classier 22 3.2.4 Post-Processing 23 3.3 Experiments 24 3.4 Summary 27 4 Audio Tagging using Local Detector and Global Classier 29 4.1 Introduction 29 4.2 CNN-Based Audio Tagging Model 31 4.2.1 Local Detector and Global Classier 32 4.2.2 Temporal Localization of Events 34 4.3 Experiments 34 4.3.1 Dataset and Feature 34 4.3.2 Model Training 35 4.3.3 Results 36 4.4 Summary 39 5 Deep Convolutional Neural Network with Structured Prediction for Weakly Supervised Audio Event Detection 41 5.1 Introduction 41 5.2 CNN with Structured Prediction for Weakly Supervised AED 46 5.2.1 DenseNet 47 5.2.2 Squeeze-and-Excitation 48 5.2.3 Global Pooling for Aggregation 49 5.2.4 Structured Prediction for Accurate Event Localization 50 5.3 Experiments 53 5.3.1 Dataset 53 5.3.2 Feature Extraction 54 5.3.3 DSNet and DSNet-RNN Structures 54 5.3.4 Baseline CNN Structure 56 5.3.5 Training and Evaluation 57 5.3.6 Metrics 57 5.3.7 Results and Discussion 58 5.3.8 Comparison with the DCASE 2017 task 4 Results 61 5.4 Summary 62 6 Conclusions 65 Bibliography 67 요 약 77 감사의 글 79Docto

Experiments on the DCASE Challenge 2016: Acoustic Scene Classification and Sound Event Detection in Real Life Recording

In this paper we present our work on Task 1 Acoustic Scene Classi- fication and Task 3 Sound Event Detection in Real Life Recordings. Among our experiments we have low-level and high-level features, classifier optimization and other heuristics specific to each task. Our performance for both tasks improved the baseline from DCASE: for Task 1 we achieved an overall accuracy of 78.9% compared to the baseline of 72.6% and for Task 3 we achieved a Segment-Based Error Rate of 0.76 compared to the baseline of 0.91