2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)最新文献_第3页

Estimation of Angular Power Spectrum Using Multikernel Adaptive Filtering 用多核自适应滤波估计角功率谱

2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2022-11-07 DOI: 10.23919/APSIPAASC55919.2022.9980067

Eiji Ninomiya, M. Yukawa, Renato L. G. Cavalcante, Lorenzo Miretti

引用次数: 0

C-CycleTransGAN: A Non-parallel Controllable Cross-gender Voice Conversion Model with CycleGAN and Transformer C-CycleTransGAN:一个具有CycleGAN和变压器的非并行可控跨性别语音转换模型

2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2022-11-07 DOI: 10.23919/APSIPAASC55919.2022.9979821

Changzeng Fu, Chaoran Liu, C. Ishi, H. Ishiguro

{"title":"C-CycleTransGAN: A Non-parallel Controllable Cross-gender Voice Conversion Model with CycleGAN and Transformer","authors":"Changzeng Fu, Chaoran Liu, C. Ishi, H. Ishiguro","doi":"10.23919/APSIPAASC55919.2022.9979821","DOIUrl":"https://doi.org/10.23919/APSIPAASC55919.2022.9979821","url":null,"abstract":"In this study, we propose a conversion intensity controllable model for the cross-gender voice conversion (VC)11Demo page can be found at https://cz26.github.io/DemoPage-c-CycleTransGAN-VoiceConversion/. In particular, we combine the CycleGAN and transformer module, and build a condition embedding network as an intensity controller. The model is firstly pre-trained with self-supervised learning on the single-gender voice reconstruction task, with the condition set to male-to-male or female-to-female. Then, we fine-tune the model on the cross-gender voice conversion task after the pretraining is completed, with the condition set to male-to-female or female-to-male. In the testing procedure, the condition is expected to be employed as a controllable parameter (scale) to adjust the conversion intensity. The proposed method was evaluated on the Voice Conversion Challenge dataset and compared to two baselines (CycleGAN, CycleTransGAN) with objective and subjective evaluations. The results show that our proposed model is able to equip the model with an additional function of cross-gender controllability and without hurting the voice conversion performance.","PeriodicalId":382967,"journal":{"name":"2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131218551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Highly Robust Action Retrieval using View-invariant Pose Feature and Simple yet Effective Query Expansion Method 基于视图不变姿态特征的高鲁棒动作检索和简单有效的查询扩展方法

2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2022-11-07 DOI: 10.23919/APSIPAASC55919.2022.9979865

Noboru Yoshida, Jianquan Liu

引用次数: 0

Neural Network Based Watermarking Trained with Quantized Activation Function 基于量化激活函数训练的神经网络水印

2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2022-11-07 DOI: 10.23919/APSIPAASC55919.2022.9980204

Shingo Yamauchi, Masaki Kawamura

{"title":"Neural Network Based Watermarking Trained with Quantized Activation Function","authors":"Shingo Yamauchi, Masaki Kawamura","doi":"10.23919/APSIPAASC55919.2022.9980204","DOIUrl":"https://doi.org/10.23919/APSIPAASC55919.2022.9980204","url":null,"abstract":"We propose a watermarking method that incor-porates a quantized activation function to provide robustness against quantization. Zhu et al. showed that the introduction of a noise layer between the encoder and decoder can increase the robustness against attacks. Although there are various attacks on stego-images, these images are often JPEG-compressed. As the process of JPEG compression includes quantization, the watermark decoder must be able to estimate watermarks from compressed images. Hence, we propose a quantization layer that introduces a quantized activation function consisting of the hy-perbolic tangent function. The proposed neural network is based on that proposed by Hamamoto and Kawamura. By simulating the quantization of JPEG compression, the quantization layer is expected to improve the robustness against JPEG compression. The robustness was evaluated by the bit error rate (BER), and the stego-image quality was evaluated by the peak signal-to-noise ratio (PSNR). The proposed network achieved a high image quality of more than 35 dB, and it could extract watermarks with a BER of less than 0.1 for Q-values of 30 or higher in JPEG compression. It was thus more robust against JPEG compression than Hamamoto and Kawamura's model.","PeriodicalId":382967,"journal":{"name":"2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133546980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neural Vocoder Feature Estimation for Dry Singing Voice Separation 干唱腔分离的神经声码器特征估计

2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2022-11-07 DOI: 10.23919/APSIPAASC55919.2022.9980093

Jae-Yeol Im, Soonbeom Choi, Sangeon Yong, Juhan Nam

{"title":"Neural Vocoder Feature Estimation for Dry Singing Voice Separation","authors":"Jae-Yeol Im, Soonbeom Choi, Sangeon Yong, Juhan Nam","doi":"10.23919/APSIPAASC55919.2022.9980093","DOIUrl":"https://doi.org/10.23919/APSIPAASC55919.2022.9980093","url":null,"abstract":"Singing voice separation (SVS) is a task that separates singing voice audio from its mixture with instrumental audio. Previous SVS studies have mainly employed the spectrogram masking method which requires a large dimensionality in predicting the binary masks. In addition, they focused on extracting a vocal stem that retains the wet sound with the reverberation effect. This result may hinder the reusability of the isolated singing voice. This paper addresses the issues by predicting mel-spectrogram of dry singing voices from the mixed audio as neural vocoder features and synthesizing the singing voice waveforms from the neural vocoder. We experimented with two separation methods. One is predicting binary masks in the mel-spectrogram domain and the other is directly predicting the mel-spectrogram. Furthermore, we add a singing voice detector to identify the singing voice segments over time more explicitly. We measured the model performance in terms of audio, dereverberation, separation, and overall quality. The results show that our proposed model outperforms state-of-the-art singing voice separation models in both objective and subjective evaluation except the audio quality.","PeriodicalId":382967,"journal":{"name":"2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132186695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Syllable Analysis Data Augmentation for Khmer Ancient Palm leaf Recognition 高棉古棕榈叶识别的音节分析数据增强

2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2022-11-07 DOI: 10.23919/APSIPAASC55919.2022.9980217

Nimol Thuon, Jun Du, Jianshu Zhang

{"title":"Syllable Analysis Data Augmentation for Khmer Ancient Palm leaf Recognition","authors":"Nimol Thuon, Jun Du, Jianshu Zhang","doi":"10.23919/APSIPAASC55919.2022.9980217","DOIUrl":"https://doi.org/10.23919/APSIPAASC55919.2022.9980217","url":null,"abstract":"The unique forms and physical conditions of the Khmer palm leaf manuscript recognition system are receiving more attention from researchers. In the state-of-the-art, data augmentation is commonly used for data training; however, grammatical mistakes and data availability in the training process would determine or limit the accuracy rate. The two significant challenges lie in (1) grammar complexity and (2) wording similarity; therefore, this paper presents the Syllable Analysis Data Augmentation (SADA) technique, which aims at boosting the accuracy of the text recognition system for one of Southeast Asia's historical manuscripts from Cambodia. SADA comprises two fundamental modules: (1) formulating a collection of syllables/words to structure glyph patterns and (2) generating patterns from existing data through augmentation techniques and utilizing flexible geometric image transformation to increase similar word/text images. Initially, image collections are established, whereby datasets are interpreted according to the reordered grammatical structures to construct multiple glyph images. Next, we aim at conducting the experiment with a text/word recognition system before regulating attention-based encoder-decoder to enhance the probability of transcriptions of low and high-resolution images. At last, the experiment centers on datasets from various sources, including public datasets from ICFHR 2018 contest and our new augmentation datasets, all of which aim at demonstrating and evaluating the accuracy of the findings.","PeriodicalId":382967,"journal":{"name":"2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115420373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DBR: A Depth-Branch-Resorting Algorithm for Locality Exploration in Graph Processing 基于深度分支的图处理局部探索算法

2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2022-11-07 DOI: 10.23919/APSIPAASC55919.2022.9980127

Lin Jiang, Ru Feng, Junjie Wang, Junyong Deng

引用次数: 0

Nonlinear Residual Echo Suppression Based on Gated Dual Signal Transformation LSTM Network 基于门控对偶信号变换LSTM网络的非线性残留回波抑制

2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2022-11-07 DOI: 10.23919/APSIPAASC55919.2022.9980060

Kai Xie, Ziye Yang, Jie Chen

引用次数: 0

Adapted Spectrogram Transformer for Unsupervised Cross-Domain Acoustic Anomaly Detection 无监督跨域声学异常检测的自适应谱图变换

2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2022-11-07 DOI: 10.23919/APSIPAASC55919.2022.9980266

Gilles Van De Vyver, Zhaoyi Liu, Koustabh Dolui, D. Hughes, Sam Michiels

{"title":"Adapted Spectrogram Transformer for Unsupervised Cross-Domain Acoustic Anomaly Detection","authors":"Gilles Van De Vyver, Zhaoyi Liu, Koustabh Dolui, D. Hughes, Sam Michiels","doi":"10.23919/APSIPAASC55919.2022.9980266","DOIUrl":"https://doi.org/10.23919/APSIPAASC55919.2022.9980266","url":null,"abstract":"Anomaly detection models can help to automatically and proactively detect faults in industrial machines. Microphones are appealing as they are generally inexpensive and unlike visual inspection, recording sound samples can give information about the internals of the machine. However, conventional methods based on an AutoEncoder (AE) structure learned from scratch generally struggle to learn how to robustly reconstruct samples with limited available data. This paper addresses this problem by presenting a method for unsupervised Acoustic Anomaly Detection (AAD) that adapts intermediate embeddings from a pretrained, self-attention-based spectrogram transformer. Transfer learning from a large, successful model offers a solution to learning with limited data by reusing external knowledge. For AAD, this can help to recognize subtle anomalies. This work proposes two method variants that take advantage of Intermediate Feature Embeddings (IFEs) from the Audio Spectrogram Transformer (AST). The first fits a Gaussian Mixture Model (GMM) on the IFEs produced by intermediate layers of the AST. We call this ADIFAST: Anomaly Detection from Intermediate Features extracted from AST. The second uses the IFEs in a different, more effective way by adapting the AST to an AE structure. We call it TELD: Transformer Encoder Linear Decoder network. The relationship between the two method variants is that they both take advantage of the IFEs extracted by the AST. Evaluating TELD on task 2 of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2021 challenge gives an average improvement to the Area Under Curve (AUC) score of 3.9% for binary labeling normal and anomalous samples in the target domain.","PeriodicalId":382967,"journal":{"name":"2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123919003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligibility prediction of enhanced speech using recognition accuracy of end-to-end ASR systems 基于端到端ASR系统识别精度的增强语音可理解性预测

2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2022-11-07 DOI: 10.23919/APSIPAASC55919.2022.9980257

Kenichi Arai, A. Ogawa, S. Araki, K. Kinoshita, T. Nakatani, Naoyuki Kamo, T. Irino

{"title":"Intelligibility prediction of enhanced speech using recognition accuracy of end-to-end ASR systems","authors":"Kenichi Arai, A. Ogawa, S. Araki, K. Kinoshita, T. Nakatani, Naoyuki Kamo, T. Irino","doi":"10.23919/APSIPAASC55919.2022.9980257","DOIUrl":"https://doi.org/10.23919/APSIPAASC55919.2022.9980257","url":null,"abstract":"We propose speech intelligibility (SI) prediction methods using the recognition accuracy of an end-to-end (E2E) automatic speech recognition (ASR) system whose ASR performance has become comparable to the human auditory system due to its recent significant progress. Such predictors will fuel the development of speech enhancement methods for human listeners. In this paper, we evaluate our proposed method's prediction performance of the intelligibility of enhanced noisy speech signals. Our experiments show that when ASR systems are trained with various noisy speech data, our proposed methods, which do not require clean reference signals, predict SI more accurately than the existing “intrusive” methods: short-time objective intelligibility (STOI), extended-STOI (eSTOI), and our previously proposed methods, which were based on deep neural network-hidden Markov model hybrid ASR systems. Our experiments also show that our method, which additionally uses clean speech for determining the speech region of evaluation signals, further improves the prediction accuracy more than the existing methods.","PeriodicalId":382967,"journal":{"name":"2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"280 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124487764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1