2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics最新文献_第2页

Influence of secondary path estimation errors on the performance of ANC-motivated noise reduction algorithms for hearing aids 辅助路径估计误差对助听器ac驱动降噪算法性能的影响

2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics Pub Date : 2013-10-01 DOI: 10.1109/WASPAA.2013.6701812

Derya Dalga, S. Doclo

{"title":"Influence of secondary path estimation errors on the performance of ANC-motivated noise reduction algorithms for hearing aids","authors":"Derya Dalga, S. Doclo","doi":"10.1109/WASPAA.2013.6701812","DOIUrl":"https://doi.org/10.1109/WASPAA.2013.6701812","url":null,"abstract":"Current noise reduction techniques for open-fitting hearing aids that only use the external microphones on the hearing aid typically disregard the occurrence of signal leakage through the open fitting, leading to a degraded noise reduction performance. Using an ear mould with an internal (so-called error) microphone provides information about the signal leakage and hence enables to improve the noise reduction performance. Recently, feedforward and combined feedforward-feedback active-noise-control-motivated (FF ANC and FF-FB ANC, respectively) algorithms for noise reduction have been presented for such open-fitting hearing aids. The noise reduction filters of these ANC-motivated algorithms depend on an estimate of the so-called secondary path between the receiver and the error microphone. In this paper, we analyze the influence of secondary path estimation errors on the performance of the ANC-motivated algorithms. For the FF ANC algorithm it is possible to derive a closed-form expression of the filter as a function of the secondary path estimation error and to derive limit values for the allowable secondary path estimation errors. In addition, simulations show that even when estimation errors occur the FF-FB ANC algorithm still outperforms the FF ANC algorithm.","PeriodicalId":341888,"journal":{"name":"2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115925803","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

A simple adaptive cardioid direction finding algorithm 一个简单的自适应心脏测向算法

2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics Pub Date : 2013-10-01 DOI: 10.1109/WASPAA.2013.6701831

G. Elko, Jens Meyer

引用次数: 1

A sparse nonuniformly partitioned multidelay filter for acoustic echo cancellation 一种用于回声消除的稀疏非均匀分割多延迟滤波器

2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics Pub Date : 2013-10-01 DOI: 10.1109/WASPAA.2013.6701832

D. Giacobello, Joshua Atkins

引用次数: 2

Geometrically Constrained TRINICON-based relative transfer function estimation in underdetermined scenarios 欠确定场景下基于几何约束trinicon的相对传递函数估计

2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics Pub Date : 2013-10-01 DOI: 10.1109/WASPAA.2013.6701822

K. Reindl, S. M. Golan, Hendrik Barfuss, S. Gannot, Walter Kellermann

引用次数: 31

Acoustic scene classification using sparse feature learning and event-based pooling 基于稀疏特征学习和事件池的声学场景分类

2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics Pub Date : 2013-10-01 DOI: 10.1109/WASPAA.2013.6701893

Kyogu Lee, Ziwon Hyung, Juhan Nam

{"title":"Acoustic scene classification using sparse feature learning and event-based pooling","authors":"Kyogu Lee, Ziwon Hyung, Juhan Nam","doi":"10.1109/WASPAA.2013.6701893","DOIUrl":"https://doi.org/10.1109/WASPAA.2013.6701893","url":null,"abstract":"Recently unsupervised learning algorithms have been successfully used to represent data in many of machine recognition tasks. In particular, sparse feature learning algorithms have shown that they can not only discover meaningful structures from raw data but also outperform many hand-engineered features. In this paper, we apply the sparse feature learning approach to acoustic scene classification. We use a sparse restricted Boltzmann machine to capture manyfold local acoustic structures from audio data and represent the data in a high-dimensional sparse feature space given the learned structures. For scene classification, we summarize the local features by pooling over audio scene data. While the feature pooling is typically performed over uniformly divided segments, we suggest a new pooling method, which first detects audio events and then performs pooling only over detected events, considering the irregular occurrence of audio events in acoustic scene data. We evaluate the learned features on the IEEE AASP Challenge development set, comparing them with a baseline model using mel-frequency cepstral coefficients (MFCCs). The results show that learned features outperform MFCCs, event-based pooling achieves higher accuracy than uniform pooling and, furthermore, a combination of the two methods performs even better than either one used alone.","PeriodicalId":341888,"journal":{"name":"2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123913954","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: 29

Environment-aware ideal binary mask estimation using monaural cues 基于单信号的环境感知理想二值掩码估计

2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics Pub Date : 2013-10-01 DOI: 10.1109/WASPAA.2013.6701821

T. May, T. Dau

引用次数: 15

Learning an intelligibility map of individual utterances 学习单个话语的可理解度图

2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics Pub Date : 2013-10-01 DOI: 10.1109/WASPAA.2013.6701835

Michael I. Mandel

引用次数: 6

Speech understanding in noise provided by a simulated cochlear implant processor based on matching pursuit 基于匹配追踪的模拟人工耳蜗处理器提供噪声环境下的语音理解

2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics Pub Date : 2013-10-01 DOI: 10.1109/WASPAA.2013.6701878

A. Kressner, C. Rozell

引用次数: 1

Large-scale audio feature extraction and SVM for acoustic scene classification 大规模音频特征提取与支持向量机声学场景分类

2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics Pub Date : 2013-10-01 DOI: 10.1109/WASPAA.2013.6701857

Jürgen T. Geiger, Björn Schuller, G. Rigoll

引用次数: 123

Speech enhancement for hearing instruments: Enabling communication in adverse conditions 助听器的语音增强:在不利条件下进行通信

2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics Pub Date : 2013-10-01 DOI: 10.1109/WASPAA.2013.6701897

Rainer Martin

{"title":"Speech enhancement for hearing instruments: Enabling communication in adverse conditions","authors":"Rainer Martin","doi":"10.1109/WASPAA.2013.6701897","DOIUrl":"https://doi.org/10.1109/WASPAA.2013.6701897","url":null,"abstract":"Hearing instruments are frequently used in notoriously difficult acoustic scenarios. Even for normal-hearing people ambient noise, reverberation and echoes often contribute to a degraded communication experience. The impact of these factors becomes significantly more prominent when participants suffer from a hearing loss. Nevertheless, hearing instruments are frequently used in these adverse conditions and must enable effortless communication. In this talk I will discuss challenges that are encountered in acoustic signal processing for hearing instruments. While many algorithms are motivated by the quest for a cocktail party processor and by the high-level paradigms of auditory scene analysis a careful design of statistical models and processing schemes is necessary to achieve the required performance in real world applications. Rather strict requirements result from the size of the device, the power budget, and the admissable processing latency. Starting with low-latency spectral analysis and synthesis systems for speech and music signals I will continue highlighting statistical estimation and smoothing techniques for the enhancement of noisy speech. The talk emphasizes the necessity to find a good balance between temporal and spectral resolution, processing latency, and statistical estimation errors. It concludes with single and multi-channel speech enhancement examples and an outlook towards opportunities which reside in the use of comprehensive speech processing models and distributed resources.","PeriodicalId":341888,"journal":{"name":"2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124966146","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