Polyphonic Sound Event and Sound Activity Detection: A Multi-Task Approach

| openaire: EC/H2020/637422/EU//EVERYSOUND

Polyphonic sound event localization and detection (SELD) aims at detecting types of sound events with corresponding temporal activities and spatial locations. In this paper, a trackwise ensemble event independent network with a novel data augmentation method is proposed. The proposed model is based on our previous proposed Event-Independent Network V2 and is extended by conformer blocks and dense blocks. The track-wise ensemble model with track-wise output format is proposed to solve an ensemble model problem for track-wise output format that track permutation may occur among different models. The data augmentation approach contains several data augmentation chains, which are composed of random combinations of several data augmentation operations. The method also utilizes log-mel spectrograms, intensity vectors, and Spatial Cues-Augmented Log-Spectrogram (SALSA) for different models. We evaluate our proposed method in the Task of the L3DAS22 challenge and obtain the top ranking solution with a location-dependent F-score to be 0.699. Source code is released <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .

Polyphonic sound event localization and detection (SELD) has many practical applications in acoustic sensing and monitoring. However, the development of real-time SELD has been limited by the demanding computational requirement of most recent SELD systems. In this work, we introduce SALSA-Lite, a fast and effective feature for polyphonic SELD using microphone array inputs. SALSA-Lite is a lightweight variation of a previously proposed SALSA feature for polyphonic SELD. SALSA, which stands for Spatial Cue-Augmented Log-Spectrogram, consists of multichannel log-spectrograms stacked channel-wise with the normalized principal eigenvectors of the spectrotemporally corresponding spatial covariance matrices. In contrast to SALSA, which uses eigenvector-based spatial features, SALSA-Lite uses normalized inter-channel phase differences as spatial features, allowing a 30-fold speedup compared to the original SALSA feature. Experimental results on the TAU-NIGENS Spatial Sound Events 2021 dataset showed that the SALSA-Lite feature achieved competitive performance compared to the full SALSA feature, and significantly outperformed the traditional feature set of multichannel log-mel spectrograms with generalized cross-correlation spectra. Specifically, using SALSA-Lite features increased localization-dependent F1 score and class-dependent localization recall by 15% and 5%, respectively, compared to using multichannel log-mel spectrograms with generalized cross-correlation spectra.

Sound event localization and detection have been applied in various fields. Due to the polyphony and noise interference, it becomes challenging to accurately predict the sound event and their occurrence locations. Aiming at this problem, we propose a Multiple Attention Fusion ResNet, which uses ResNet34 as the base network. Given the situation that the sound duration is not fixed, and there are multiple polyphonic and noise, we introduce the Gated Channel Transform to enhance the residual basic block. This enables the model to capture contextual information, evaluate channel weights, and reduce the interference caused by polyphony and noise. Furthermore, Split Attention is introduced to the model for capturing cross-channel information, which enhances the ability to distinguish the polyphony. Finally, Coordinate Attention is introduced to the model so that the model can focus on both the channel information and spatial location information of sound events. Experiments were conducted on two different datasets, TAU-NIGENS Spatial Sound Events 2020, and TAU-NIGENS Spatial Sound Events 2021. The results demonstrate that the proposed model significantly outperforms state-of-the-art methods under multiple polyphonic and noise-directional interference environments and it achieves competitive performance under a single polyphonic environment.

This chapter presents state-of-the-art research and open topics for analyzing complex sound scenes in a single microphone case. First, the concept of sound scene recognition is presented, from the perspective of different paradigms (classification, tagging, clustering, segmentation) and methods used. The core section is on sound event detection and classification, presenting various paradigms and practical considerations along with methods for monophonic and polyphonic sound event detection. The chapter will then focus on the concepts of context and “language modeling” for sound scenes, also covering the concept of relationships between sound events. Work on sound scene recognition based on event detection is also presented. Finally the chapter will summarize the topic and will provide directions for future research.

In this article, a special decision surface for the weakly-supervised sound event detection (SED) and a disentangled feature (DF) for the multi-label problem in polyphonic SED are proposed. We approach SED as a multiple instance learning (MIL) problem and utilize a neural network framework with a pooling module to solve it. General MIL approaches include two kinds: the instance-level approaches and embedding-level approaches. We present a method of generating instance-level probabilities for the embedding level approaches which tend to perform better than the instance-level approaches in terms of bag-level classification but can not provide instance-level probabilities in current approaches. Moreover, we further propose a specialized decision surface (SDS) for the embedding-level attention pooling. We analyze and explained why an embedding-level attention module with SDS is better than other typical pooling modules from the perspective of the high-level feature space. As for the problem of the unbalanced dataset and the co-occurrence of multiple categories in the polyphonic event detection task, we propose a DF to reduce interference among categories, which optimizes the high-level feature space by disentangling it based on class-wise identifiable information and obtaining multiple different subspaces. Experiments on the dataset of DCASE 2018 Task 4 show that the proposed SDS and DF significantly improve the detection performance of the embedding-level MIL approach with an attention pooling module and outperform the first place system in the challenge by $\mathbf {6.6}$ percentage points.

&lt;p&gt;Joint sound event localization and detection (SELD) is an emerging audio signal processing task adding spatial dimensions to acoustic scene analysis and sound event detection. A popular approach to modeling SELD jointly is using convolutional recur- rent neural network (CRNN) models, where CNNs learn high-level features from multi-channel audio input and the RNNs learn temporal relationships from these high-level features. However, RNNs have some drawbacks, such as a limited capability to model long temporal dependencies and slow training and inference times due to their sequential processing nature. Recently, a few SELD studies used multi-head self-attention (MHSA), among other innovations in their models. MHSA and the related transformer networks have shown state-of-the-art performance in various domains. While they can model long temporal dependencies, they can also be parallelized efficiently. In this paper, we study in detail the effect of MHSA on the SELD task. Specifically, we examined the effects of replacing the RNN blocks with self-attention layers. We studied the influence of stacking multiple self-attention blocks, using multiple attention heads in each self-attention block, and the effect of position embeddings and layer normalization. Evaluation on the DCASE 2021 SELD (task 3) development data set shows a significant improvement in all employed metrics compared to the baseline CRNN accompanying the task.&lt;/p&gt;

Polyphonic sound event detection (SED) is an emerging area with many applications for smart disaster safety, security, life logging, etc. This paper proposes a two-stage polyphonic SED model when strongly labeled data are limited but weakly labeled and unlabeled data are available. The first stage of the proposed SED model is constructed by a residual convolutional recurrent neural network (RCRNN)-based mean teacher model with convolutional block attention module (CBAM)-based attention. Then, the second stage fine-tunes the student model from the first stage by applying the proposed semi-supervised loss function to accommodate the noisy targets of weakly labeled and unlabeled data. The proposed SED model is applied to both Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 Challenge Task 4 and DCASE 2020 Challenge Task 4, and its performance is compared with those of the baseline and top-ranked models from both challenges by measuring the F1-score and polyphonic sound detection score (PSDS). The experiments show that the RCRNN-based first-stage model with CBAM-based attention achieves a higher F1-score and PSDS than the baseline and top-ranked models for both challenges. Furthermore, the proposed two-stage SED model with the semi-supervised loss function improves the F1-score by 6.1% and 4.6% compared to the top-ranked models from DCASE 2019 and 2020, respectively.

This paper proposes a sound event detection (SED) model using an EfficientNet-B2 and an attention and aggregation-based feature pyramid network (A2-FPN). In particular, the EfficientNet-B2 is first obtained from the pretrained model on the basis of the pretraining, sampling, labeling, and aggregation (PSLA) framework. Then, the A2-FPN module is applied to the outputs of the layers of the EfficientNet-B2 to deal with the different time and frequency resolutions from acoustic features. The aggregated feature map from the A2-FPN module is used as input features to two bidirectional gated recurrent unit layers. Specifically, the proposed A2-FPN-based SED model is trained by the mean-teacher approach to utilize weakly labeled and unlabeled data. Finally, the proposed A2-FPN-based SED model is applied to the detection and classification of acoustic scenes and events (DCASE) 2021 Challenge Task 4. Consequently, it is shown that the polyphonic sound event detection score (PSDS) 1 and 2 of the proposed A2-FPN-based SED model are the higher of 0.03 and 0.172, respectively, than those of the DCASE 2021 Challenge Task 4 baseline.

Multi-granularity acoustic information fusion for sound event detection

We investigate unsupervised learning of correspondences between sound events and textual phrases through aligning audio clips with textual captions describing the content of a whole audio clip. We align originally unaligned and unannotated audio clips and their captions by scoring the similarities between audio frames and words, as encoded by modality-specific encoders and using a ranking-loss criterion to optimize the model. After training, we obtain clip-caption similarity by averaging frame-word similarities and estimate event-phrase correspondences by calculating frame-phrase similarities. We evaluate the method with two cross-modal tasks: audio-caption retrieval, and phrase-based sound event detection (SED). Experimental results show that the proposed method can globally associate audio clips with captions as well as locally learn correspondences between individual sound events and textual phrases in an unsupervised manner.

In real-life audio scenes, many sound events from different sources are simultaneously active, which makes the automatic sound event detection challenging. In this paper, we compare two different deep learning methods for the detection of environmental sound events: combined single-label classification and multi-label classification. We investigate the accuracy of both methods on the audio with different levels of polyphony. Multi-label classification achieves an overall 62.8% accuracy, whereas combined single-label classification achieves a very close 61.9% accuracy. The latter approach offers more flexibility on real-world applications by gathering the relevant group of sound events in a single classifier with various combinations.

This study proposes a sound event localization and detection (SELD) method using imbalanced real and synthetic data via a multi-generator. The proposed method is based on a residual convolutional neural network (RCNN) and a transformer encoder for real spatial sound scenes. SELD aims to classify the sound event, detect the onset and offset of the classified event, and estimate the direction of the sound event. In Detection and Classification of Acoustic Scenes and Events (DCASE) 2022 Task 3, SELD is performed with a few real spatial sound scene data and a relatively large number of synthetic data. When a model is trained using imbalanced data, it can proceed by focusing only on a larger number of data. Thus, a multi-generator that samples real and synthetic data at a specific rate in one batch is proposed to prevent this problem. We applied the data augmentation technique SpecAugment and used time-frequency masking to the dataset. Furthermore, we propose a neural network architecture to apply the RCNN and transformer encoder. Several models were trained with various structures and hyperparameters, and several ensemble models were obtained by “cherry-picking” specific models. Based on the experiment, the single model of the proposed method and the model applied with the ensemble exhibited improved performance compared with the baseline model.

In order to improve the accuracy of sound source localization based on depth learning and reduce the amount of model calculation. In this paper, a principal component analysis sound event localization and detection network (PCA-SELDnet) based on convolution recurrent network model (SELDnet) is proposed, which introduces time domain feature weighted phase transition generalized cross-correlation (GCC-PHAT) and uses principal component analysis (PCA) carries out dimensionality reduction and reconstruction, obtains the features of several dimensions with large contribution, and then combines them with the amplitude spectrum of the original spectrogram for input. The combined features are mapped into two output matrices through SELDnet model, one output is sound event detection matrix and the other output is sound source localization matrix. The results show that PCA-SELDnet improves the accuracy of sound event localization.

A technique for detection of sound events based on the attention and gestalt perception, two basic principles that are widely used by man for detection and recognition of sound events, is discussed. In particular, an array of cochlear perceptors (ACP) is proposed as a tool for analysis of a sound scene and as a suitable basis for implementation of both principles. An input signal is described by an intensity pattern and by a pattern of temporal changes. The implementation of both patterns in sound event detection is described, and the problems of frequency analysis of the sound scene are discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>