ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)最新文献_第10页

Network Adaptation Strategies for Learning New Classes without Forgetting the Original Ones 学习新课程不忘原课程的网络适应策略

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8682848

Hagai Taitelbaum, Gal Chechik, J. Goldberger

引用次数: 2

Introducing the Orthogonal Periodic Sequences for the Identification of Functional Link Polynomial Filters 引入正交周期序列用于函数链多项式滤波器的辨识

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8683342

A. Carini, S. Orcioni, S. Cecchi

引用次数: 3

Performance Analysis of Convex Data Detection in MIMO MIMO中凸数据检测性能分析

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8683890

Ehsan Abbasi, Fariborz Salehi, B. Hassibi

{"title":"Performance Analysis of Convex Data Detection in MIMO","authors":"Ehsan Abbasi, Fariborz Salehi, B. Hassibi","doi":"10.1109/ICASSP.2019.8683890","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8683890","url":null,"abstract":"We study the performance of a convex data detection method in large multiple-input multiple-output (MIMO) systems. The goal is to recover an n-dimensional complex signal whose entries are from an arbitrary constellation $mathcal{D} subset mathbb{C}$, using m noisy linear measurements. Since the Maximum Likelihood (ML) estimation involves minimizing a loss function over the discrete set ${mathcal{D}^n}$, it becomes computationally intractable for large n. One approach is to relax to a $mathcal{D}$ convex set and to utilize convex programing to solve the problem precise and then to map the answer to the closest point in the set $mathcal{D}$. We assume an i.i.d. complex Gaussian channel matrix and derive expressions for the symbol error probability of the proposed convex method in the limit of m, n → ∞. Prior work was only able to do so for real valued constellations such as BPSK and PAM. The main contribution of this paper is to extend the results to complex valued constellations. In particular, we use our main theorem to calculate the performance of the complex algorithm for PSK and QAM constellations. In addition, we introduce a closed-form formula for the symbol error probability in the high-SNR regime and determine the minimum number of measurements m required for consistent signal recovery.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"66 1","pages":"4554-4558"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90291360","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: 16

Automatic Transcription of Diatonic Harmonica Recordings 自动转录的全音阶口琴录音

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8682334

Filipe M. Lins, M. Johann, Emmanouil Benetos, Rodrigo Schramm

{"title":"Automatic Transcription of Diatonic Harmonica Recordings","authors":"Filipe M. Lins, M. Johann, Emmanouil Benetos, Rodrigo Schramm","doi":"10.1109/ICASSP.2019.8682334","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8682334","url":null,"abstract":"This paper presents a method for automatic transcription of the diatonic Harmonica instrument. It estimates the multi-pitch activations through a spectrogram factorisation framework. This framework is based on Probabilistic Latent Component Analysis (PLCA) and uses a fixed 4-dimensional dictionary with spectral templates extracted from Harmonica’s instrument timbre. Methods based on spectrogram factorisation may suffer from local-optima issues in the presence of harmonic overlap or considerable timbre variability. To alleviate this issue, we propose a set of harmonic constraints that are inherent to the Harmonica instrument note layout or are caused by specific diatonic Harmonica playing techniques. These constraints help to guide the factorisation process until convergence into meaningful multi-pitch activations is achieved. This work also builds a new audio dataset containing solo recordings of diatonic Harmonica excerpts and the respective multi-pitch annotations. We compare our proposed approach against multiple baseline techniques for automatic music transcription on this dataset and report the results based on frame-based F-measure statistics.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"29 1","pages":"256-260"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90299941","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

Imitation Refinement for X-ray Diffraction Signal Processing x射线衍射信号处理的模拟改进

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8683723

Junwen Bai, Zihang Lai, Runzhe Yang, Yexiang Xue, J. Gregoire, C. Gomes

{"title":"Imitation Refinement for X-ray Diffraction Signal Processing","authors":"Junwen Bai, Zihang Lai, Runzhe Yang, Yexiang Xue, J. Gregoire, C. Gomes","doi":"10.1109/ICASSP.2019.8683723","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8683723","url":null,"abstract":"Many real-world tasks involve identifying signals from data satisfying background or prior knowledge. In domains like materials discovery, due to the flaws and biases in raw experimental data, the identification of X-ray diffraction (XRD) signals often requires significant (manual) expert work to find refined signals that are similar to the ideal theoretical ones. Automatically refining the raw XRD signals utilizing simulated theoretical data is thus desirable. We propose imitation refinement, a novel approach to refine imperfect input signals, guided by a pre-trained classifier incorporating prior knowledge from simulated theoretical data, such that the refined signals imitate the ideal ones. The classifier is trained on the ideal simulated data to classify signals and learns an embedding space where each class is represented by a prototype. The refiner learns to refine the imperfect signals with small modifications, such that their embeddings are closer to the corresponding prototypes. We show that the refiner can be trained in both supervised and unsupervised fashions. We further illustrate the effectiveness of the proposed approach both qualitatively and quantitatively in an X-ray diffraction signal refinement task in materials discovery.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"46 1","pages":"3337-3341"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90311979","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

Context Modelling Using Hierarchical Attention Networks for Sentiment and Self-assessed Emotion Detection in Spoken Narratives 基于层次注意网络的语境建模在口语叙事中的情绪和自我评估情绪检测

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8683801

Lukas Stappen, N. Cummins, Eva-Maria Messner, H. Baumeister, J. Dineley, Björn Schuller

{"title":"Context Modelling Using Hierarchical Attention Networks for Sentiment and Self-assessed Emotion Detection in Spoken Narratives","authors":"Lukas Stappen, N. Cummins, Eva-Maria Messner, H. Baumeister, J. Dineley, Björn Schuller","doi":"10.1109/ICASSP.2019.8683801","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8683801","url":null,"abstract":"Automatic detection of sentiment and affect in personal narratives through word usage has the potential to assist in the automated detection of change in psychotherapy. Such a tool could, for instance, provide an efficient, objective measure of the time a person has been in a positive or negative state-of-mind. Towards this goal, we propose and develop a hierarchical attention model for the tasks of sentiment (positive and negative) and self-assessed affect detection in transcripts of personal narratives. We also perform a qualitative analysis of the word attentions learnt by our sentiment analysis model. In a key result, our attention model achieved an un-weighted average recall (UAR) of 91.0 % in a binary sentiment detection task on the test partition of the Ulm State-of-Mind in Speech (USoMS) corpus. We also achieved UARs of 73.7 % and 68.6 % in the 3-class tasks of arousal and valence detection respectively. Finally, our qualitative analysis associates colloquial reinforcements with positive sentiments, and uncertain phrasing with negative sentiments.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"47 14","pages":"6680-6684"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91435895","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: 12

Joint Separation and Dereverberation of Reverberant Mixtures with Multichannel Variational Autoencoder 多声道变分自编码器混响混响的联合分离与去噪

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8683497

S. Inoue, H. Kameoka, Li Li, Shogo Seki, S. Makino

{"title":"Joint Separation and Dereverberation of Reverberant Mixtures with Multichannel Variational Autoencoder","authors":"S. Inoue, H. Kameoka, Li Li, Shogo Seki, S. Makino","doi":"10.1109/ICASSP.2019.8683497","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8683497","url":null,"abstract":"In this paper, we deal with a multichannel source separation problem under a highly reverberant condition. The multichan- nel variational autoencoder (MVAE) is a recently proposed source separation method that employs the decoder distribu- tion of a conditional VAE (CVAE) as the generative model for the complex spectrograms of the underlying source sig- nals. Although MVAE is notable in that it can significantly improve the source separation performance compared with conventional methods, its capability to separate highly rever- berant mixtures is still limited since MVAE uses an instan- taneous mixture model. To overcome this limitation, in this paper we propose extending MVAE to simultaneously solve source separation and dereverberation problems by formulat- ing the separation system as a frequency-domain convolutive mixture model. A convergence-guaranteed algorithm based on the coordinate descent method is derived for the optimiza- tion. Experimental results revealed that the proposed method outperformed the conventional methods in terms of all the source separation criteria in highly reverberant environments.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"16 1","pages":"96-100"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84952009","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: 15

Look, Listen, and Learn More: Design Choices for Deep Audio Embeddings 看、听和学习更多:深度音频嵌入的设计选择

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8682475

J. Cramer, Ho-Hsiang Wu, J. Salamon, J. Bello

{"title":"Look, Listen, and Learn More: Design Choices for Deep Audio Embeddings","authors":"J. Cramer, Ho-Hsiang Wu, J. Salamon, J. Bello","doi":"10.1109/ICASSP.2019.8682475","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8682475","url":null,"abstract":"A considerable challenge in applying deep learning to audio classification is the scarcity of labeled data. An increasingly popular solution is to learn deep audio embeddings from large audio collections and use them to train shallow classifiers using small labeled datasets. Look, Listen, and Learn (L3-Net) is an embedding trained through self-supervised learning of audio-visual correspondence in videos as opposed to other embeddings requiring labeled data. This framework has the potential to produce powerful out-of-the-box embeddings for downstream audio classification tasks, but has a number of unexplained design choices that may impact the embeddings’ behavior. In this paper we investigate how L3-Net design choices impact the performance of downstream audio classifiers trained with these embeddings. We show that audio-informed choices of input representation are important, and that using sufficient data for training the embedding is key. Surprisingly, we find that matching the content for training the embedding to the downstream task is not beneficial. Finally, we show that our best variant of the L3-Net embedding outperforms both the VGGish and SoundNet embeddings, while having fewer parameters and being trained on less data. Our implementation of the L3-Net embedding model as well as pre-trained models are made freely available online.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"5 1","pages":"3852-3856"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85310875","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: 223

Solving Quadratic Equations via Amplitude-based Nonconvex Optimization 基于幅值的非凸优化求解二次方程

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8682357

Vincent Monardo, Yuanxin Li, Yuejie Chi

{"title":"Solving Quadratic Equations via Amplitude-based Nonconvex Optimization","authors":"Vincent Monardo, Yuanxin Li, Yuejie Chi","doi":"10.1109/ICASSP.2019.8682357","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8682357","url":null,"abstract":"In many signal processing tasks, one seeks to recover an r-column matrix object ${mathbf{X}} in {mathbb{C}^{n times r}}$ from a set of nonnegative quadratic measurements up to orthonormal transforms. Example applications include coherence retrieval in optical imaging and covariance sketching for high-dimensional streaming data. To this end, efficient nonconvex optimization methods are quite appealing, due to their computational efficiency and scalability to large-scale problems. There is a recent surge of activities in designing nonconvex methods for the special case r = 1, known as phase retrieval; however, very little work has studied the general rank-r setting. Motivated by the success of phase retrieval, in this paper we derive several algorithms which utilize the quadratic loss function based on amplitude measurements, including (stochastic) gradient descent and alternating minimization. Numerical experiments demonstrate their computational and statistical performances, highlighting the superior performance of stochastic gradient descent with appropriate mini-batch sizes.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"57 11","pages":"5526-5530"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91399390","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: 3

Nonlinear Acceleration of Constrained Optimization Algorithms 约束优化算法的非线性加速

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8682962

Vien V. Mai, M. Johansson

引用次数: 9