Learn More
In this paper, we present a method for time series analysis based on empirical intrinsic geometry (EIG). EIG enables one to reveal the low-dimensional parametric manifold as well as to infer the underlying dynamics of high-dimensional time series. By incorporating concepts of information geometry, this method extends existing geometric analysis tools to(More)
The goal of this study is to identify preseizure changes in intracranial EEG (icEEG). A novel approach based on the recently developed diffusion map framework, which is considered to be one of the leading manifold learning methods, is proposed. Diffusion mapping provides dimensionality reduction of the data as well as pattern recognition that can be used to(More)
The reverberation time (RT) is a very important measure that quantifies the acoustic properties of a room and provides information about the quality and intelligibility of speech recorded in that room. Moreover, information about the RT can be used to improve the performance of automatic speech recognition systems and speech dereverberation algorithms. In a(More)
Conventional speaker localization algorithms, based merely on the received microphone signals, are often sensitive to adverse conditions, such as: high reverberation or low signal-to-noise ratio (SNR). In some scenarios, e.g., in meeting rooms or cars, it can be assumed that the source position is confined to a predefined area, and the acoustic parameters(More)
Recently, we introduced a method to recover the controlling parameters of linear systems using diffusion kernels. In this paper, we apply our approach to the problem of source localization in a reverberant room using measurements from a single microphone. Prior recordings of signals from various known locations in the room are required for training and(More)
In this paper, we present a supervised graph-based framework for sequential processing and employ it to the problem of transient interference suppression. Transients typically consist of an initial peak followed by decaying short-duration oscillations. Such sounds, e.g., keyboard typing and door knocking, often arise as an interference in everyday(More)
Modeling natural and artificial systems has played a key role in various applications and has long been a task that has drawn enormous efforts. In this work, instead of exploring predefined models, we aim to identify implicitly the system degrees of freedom. This approach circumvents the dependency of a specific predefined model for a specific task or(More)
Enhancement of speech signals for hands-free communication systems has attracted significant research efforts in the last few decades. Still, many aspects and applications remain open and require further research. One of the important open problems is the single-channel transient noise reduction. In this paper, we present a novel approach for transient(More)
In this paper, we present a relative transfer function (RTF) identification method for speech sources in reverberant environments. The proposed method is based on the convolutive transfer function (CTF) approximation, which enables to represent a linear convolution in the time domain as a linear convolution in the short-time Fourier transform (STFT) domain.(More)
A transient is an abrupt or impulsive sound followed by decaying oscillations, e.g., keyboard typing and door knocking. Such sounds often arise as interference in everyday applications, e.g., hearing aids, hands-free accessories, mobile phones, and conference-room devices. In this paper, we present an algorithm for single-channel transient interference(More)