2020 28th European Signal Processing Conference (EUSIPCO)最新文献_第8页

Robust Acoustic Scene Classification to Multiple Devices Using Maximum Classifier Discrepancy and Knowledge Distillation 基于最大分类器差异和知识蒸馏的多设备声场景鲁棒分类

2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287734

Saori Takeyama, Tatsuya Komatsu, Koichi Miyazaki, M. Togami, Shunsuke Ono

引用次数: 9

Faster independent low-rank matrix analysis with pairwise updates of demixing vectors 更快的独立低秩矩阵分析与分解向量的两两更新

2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287508

Taishi Nakashima, Robin Scheibler, Yukoh Wakabayashi, Nobutaka Ono

{"title":"Faster independent low-rank matrix analysis with pairwise updates of demixing vectors","authors":"Taishi Nakashima, Robin Scheibler, Yukoh Wakabayashi, Nobutaka Ono","doi":"10.23919/Eusipco47968.2020.9287508","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287508","url":null,"abstract":"In this paper, we present an algorithm for independent low-rank matrix analysis (ILRMA) of three or more sources that is faster than that for conventional ILRMA. In conventional ILRMA, demixing vectors are updated one by one by the iterative projection (IP) method. The update rules of IP are derived from a system of quadratic equations obtained by differentiating the objective function of ILRMA with respect to demixing vectors. This system of quadratic equations is called hybrid exact-approximate joint diagonalization (HEAD) and no closed-form solution is known yet for three or more sources. Recently, a method that can update two demixing vectors simultaneously has been proposed for independent vector analysis. The method is derived by reducing HEAD for two sources to a generalized eigenvalue problem and solving the problem. Furthermore, the pairwise updates have recently been extended to the case of three or more sources. However, the efficacy of the pairwise updates for ILRMA has not yet been investigated. Therefore, in this work, we apply the pairwise updates of demixing vectors to ILRMA. By replacing the update rules of demixing vectors with the proposed pairwise updates, we accelerate the convergence of ILRMA. The experimental results show that the proposed method yields faster convergence and better performance than conventional ILRMA.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"40 1","pages":"301-305"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87612643","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: 5

A Provably Accurate Algorithm for Recovering Compactly Supported Smooth Functions from Spectrogram Measurements 从谱图测量中恢复紧支持光滑函数的一种可证明的精确算法

2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287698

Michael Perlmutter, N. Sissouno, A. Viswanathan, M. Iwen

引用次数: 0

Memory Requirement Reduction of Deep Neural Networks for Field Programmable Gate Arrays Using Low-Bit Quantization of Parameters 基于低比特参数量化的现场可编程门阵列深度神经网络内存需求降低

2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287739

Niccoló Nicodemo, Gaurav Naithani, K. Drossos, T. Virtanen, R. Saletti

引用次数: 3

PRNU-leaks: facts and remedies prnu泄漏:事实和补救措施

2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287451

F. Pérez-González, Samuel Fernández-Menduiña

引用次数: 6

CNN-based Note Onset Detection using Synthetic Data Augmentation 基于cnn的基于合成数据增强的音符起始检测

2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287621

Mina Mounir, P. Karsmakers, T. Waterschoot

{"title":"CNN-based Note Onset Detection using Synthetic Data Augmentation","authors":"Mina Mounir, P. Karsmakers, T. Waterschoot","doi":"10.23919/Eusipco47968.2020.9287621","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287621","url":null,"abstract":"Detecting the onset of notes in music excerpts is a fundamental problem in many music signal processing tasks, including analysis, synthesis, and information retrieval. When addressing the note onset detection (NOD) problem using a data-driven methodology, a major challenge is the availability and quality of labeled datasets used for both model training/tuning and evaluation. As most of the available datasets are manually annotated, the amount of annotated music excerpts is limited and the annotation strategy and quality varies across data sets. To counter both problems, in this paper we propose to use semi-synthetic datasets where the music excerpts are mixes of isolated note recordings. The advantage resides in the annotations being automatically generated while mixing the notes, as isolated note onsets are straightforward to detect using a simple energy measure. A semi-synthetic dataset is used in this work for augmenting a real piano dataset when training a convolutional Neural Network (CNN) with three novel model training strategies. Training the CNN on a semi-synthetic dataset and retraining only the CNN classification layers on a real dataset results in higher average F1-score (F1) scores with lower variance.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"89 1","pages":"171-175"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74046757","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: 2

Generic Compression of Off-The-Air Radio Frequency Signals with Grouped-Bin FFT Quantisation 分组码FFT量化对射频信号的通用压缩

2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287457

D. Muir, L. Crockett, R. Stewart

{"title":"Generic Compression of Off-The-Air Radio Frequency Signals with Grouped-Bin FFT Quantisation","authors":"D. Muir, L. Crockett, R. Stewart","doi":"10.23919/Eusipco47968.2020.9287457","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287457","url":null,"abstract":"This paper studies the capabilities of a proposed lossy, grouped-bin FFT quantisation compression method for targeting Off-The-Air (OTA) Radio Frequency (RF) signals. The bins within a 512-point Fast Fourier Transform (FFT) are split into groups of adjacent bins, and these groups are each quantised separately. Additional compression can be achieved by setting groups which are not deemed to contain significant information to zero, based on a pre-defined minimum magnitude threshold. In this paper, we propose two alternative methods for quantising the remaining groups. The first of these, Grouped-bin FFT Threshold Quantisation (GFTQ), involves allocating quantisation wordlengths based on several pre-defined magnitude thresholds. The second, Grouped-bin FFT Error Quantisation (GFEQ), involves incrementing the quantisation wordlength for each group until the calculated quantisation error falls below a minimum error threshold. Both algorithms were tested for a variety of signal types, including Digital Private Mobile Radio 446 MHz (dPMR446), which was considered as a case study. While GFTQ allowed for higher Compression Ratios (CR), the compression process resulted in added quantisation noise. The GFEQ algorithm achieved lower CRs, but also lower noise levels across all test signals.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"1 1","pages":"1767-1771"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74000525","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

Computational Approach to Track Beats in Improvisational Music Performance 在即兴音乐表演中追踪节拍的计算方法

2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287444

Xianghui Xie, Jared Houghtaling, K. Foubert, T. Waterschoot

{"title":"Computational Approach to Track Beats in Improvisational Music Performance","authors":"Xianghui Xie, Jared Houghtaling, K. Foubert, T. Waterschoot","doi":"10.23919/Eusipco47968.2020.9287444","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287444","url":null,"abstract":"Beat tracking, or identifying the temporal locations of beats in a musical recording, has a variety of applications that range from music information retrieval to machine listening. Algorithms designed to monitor the tempo of a musical recording have thus far been optimized for music with relatively stable rhythms, repetitive structures, and consistent melodies; these algorithms typically struggle to follow the free-form nature of improvisational music. Here, we present a multi-agent improvisation beat tracker (MAIBT) that addresses the challenges posed by improvisations and compare its performance with other state-of-the-art methods on a unique data set collected during improvisational music therapy sessions. This algorithm is designed for MIDI files and proceeds in four stages: (1) preprocessing to remove notes that are timid and overlapping, (2) clustering of the remaining notes and subsequent ranking of the clusters, (3) agent initialization and performance-based selection, and (4) artificial beat insertion and deletion to fill remaining beat gaps and create a comprehensive beat sequence. This particular method performs better than other generic beat-tracking approaches for music that lacks regularity; it is thus well suited to applications where unpredictability and inaccuracy are predominant, such as in music therapy improvisation.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"11 2 1","pages":"166-170"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72731551","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

One-Class based learning for Hybrid Spectrum Sensing in Cognitive Radio 基于一类学习的认知无线电混合频谱感知

2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287326

M. Jaber, A. Nasser, N. Charara, A. Mansour, K. Yao

{"title":"One-Class based learning for Hybrid Spectrum Sensing in Cognitive Radio","authors":"M. Jaber, A. Nasser, N. Charara, A. Mansour, K. Yao","doi":"10.23919/Eusipco47968.2020.9287326","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287326","url":null,"abstract":"The main aim of the Spectrum Sensing (SS) in a Cognitive Radio system is to distinguish between the binary hypotheses H0: Primary User (PU) is absent and H1: PU is active. In this paper, Machine Learning (ML)-based hybrid Spectrum Sensing (SS) scheme is proposed. The scattering of the Test Statistics (TSs) of two detectors is used in the learning and prediction phases. As the SS decision is binary, the proposed scheme requires the learning of only the boundaries of H0-class in order to make a decision on the PU status: active or idle. Thus, a set of data generated under H0 hypothesis is used to train the detection system. Accordingly, unlike the existing ML-based schemes of the literature, no PU statistical parameters are required. In order to discriminate between H0-class and elsewhere, we used a one-class classification approach that is inspired by the Isolation Forest algorithm. Extensive simulations are done in order to investigate the efficiency of such hybrid SS and the impact of the novelty detection model parameters on the detection performance. Indeed, these simulations corroborate the efficiency of the proposed one-class learning of the hybrid SS system.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"1 1","pages":"1683-1686"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77083788","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: 2

Audio-Visual Speech Classification based on Absent Class Detection 基于缺席类检测的视听语音分类

2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287615

G. D. Sad, J. Gómez

引用次数: 1