Audio Stream - Explore the Science & Experts | ideXlab


Scan Science and Technology

Contact Leading Edge Experts & Companies

Audio Stream

The Experts below are selected from a list of 9561 Experts worldwide ranked by ideXlab platform

Audio Stream – Free Register to Access Experts & Abstracts

Elizabeth A Croft – One of the best experts on this subject based on the ideXlab platform.

  • galvanic skin response derived bookmarking of an Audio Stream
    Human Factors in Computing Systems, 2011
    Co-Authors: Matthew K X J Pan, Gordon Jihshiang Chang, Gokhan H Himmetoglu, Ajung Moon, Thomas W Hazelton, Karon E Maclean, Elizabeth A Croft

    Abstract:

    We demonstrate a novel interaction paradigm driven by implicit, low-attention user control, accomplished by monitoring a user’s physiological state. We have designed and prototyped this interaction for a first use case of bookmarking an Audio Stream, to holistically explore the implicit interaction concept. A listener’s galvanic skin conductance (GSR) is monitored for orienting responses (ORs) to external interruptions; our research prototype then automatically bookmarks the media such that the user can attend to the interruption, then resume listening from the point heshe is interrupted.

    Free Register to Access Article

  • CHI Extended Abstracts – Galvanic skin response-derived bookmarking of an Audio Stream
    Proceedings of the 2011 annual conference extended abstracts on Human factors in computing systems – CHI EA '11, 2011
    Co-Authors: Matthew K X J Pan, Gordon Jihshiang Chang, Gokhan H Himmetoglu, Ajung Moon, Thomas W Hazelton, Karon E Maclean, Elizabeth A Croft

    Abstract:

    We demonstrate a novel interaction paradigm driven by implicit, low-attention user control, accomplished by monitoring a user’s physiological state. We have designed and prototyped this interaction for a first use case of bookmarking an Audio Stream, to holistically explore the implicit interaction concept. A listener’s galvanic skin conductance (GSR) is monitored for orienting responses (ORs) to external interruptions; our research prototype then automatically bookmarks the media such that the user can attend to the interruption, then resume listening from the point heshe is interrupted.

    Free Register to Access Article

Hong-jiang Zhang – One of the best experts on this subject based on the ideXlab platform.

  • Highlight sound effects detection in Audio Stream
    2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698), 2003
    Co-Authors: Rui Cai, Lie Lu, Hong-jiang Zhang, Lian-hong Cai

    Abstract:

    This paper addresses the problem of highlight sound effects detection in Audio Stream, which is very useful in fields of video summarization and highlight extraction. Unlike researches on Audio segmentation and classification, in this domain, it just locates those highlight sound effects in Audio Stream. An extensible framework is proposed and in current system three sound effects are considered: laughter, applause and cheer, which are tied up with highlight events in entertainments, sports, meetings and home videos. HMMs are used to model these sound effects and a log-likelihood scores based method is used to make final decision. A sound effect attention model is also proposed to extend general Audio attention model for highlight extraction and video summarization. Evaluations on a 2-hours Audio database showed very encouraging results.

    Free Register to Access Article

  • ICME – Highlight sound effects detection in Audio Stream
    2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698), 2003
    Co-Authors: Rui Cai, Hong-jiang Zhang, Lian-hong Cai

    Abstract:

    This paper addresses the problem of highlight sound effects detection in Audio Stream, which is very useful in fields of video summarization and highlight extraction. Unlike researches on Audio segmentation and classification, in this domain, it just locates those highlight sound effects in Audio Stream. An extensible framework is proposed and in current system three sound effects are considered: laughter, applause and cheer, which are tied up with highlight events in entertainments, sports, meetings and home videos. HMMs are used to model these sound effects and a log-likelihood scores based method is used to make final decision. A sound effect attention model is also proposed to extend general Audio attention model for highlight extraction and video summarization. Evaluations on a 2-hours Audio database showed very encouraging results.

    Free Register to Access Article

  • a robust Audio classification and segmentation method
    ACM Multimedia, 2001
    Co-Authors: Hao Jiang, Hong-jiang Zhang

    Abstract:

    In this paper, we present a robust algorithm for Audio classification that is capable of segmenting and classifying an Audio Stream into speech, music, environment sound and silence. Audio classification is processed in two steps, which makes it suitable for different applications. The first step of the classification is speech and non-speech discrimination. In this step, a novel algorithm based on KNN and LSP VQ is presented. The second step further divides non-speech class into music, environment sounds and silence with a rule based classification scheme. Some new features such as the noise frame ratio and band periodicity are introduced and discussed in detail. Our experiments in the context of video structure parsing have shown the algorithms produce very satisfactory results.

    Free Register to Access Article

Shigeki Sagayama – One of the best experts on this subject based on the ideXlab platform.

  • Audio Stream segregation of multi pitch music signal based on time space clustering using gaussian kernel 2 dimensional model
    International Conference on Acoustics Speech and Signal Processing, 2005
    Co-Authors: Hirokazu Kameoka, Takuya Nishimoto, Shigeki Sagayama

    Abstract:

    The paper describes a novel approach for Audio Stream segregation of a multi-pitch music signal. We propose a parameter-constrained time-frequency spectrum model expressing both a harmonic spectral structure and a temporal curve of the power envelope with Gaussian kernels. MAP estimation of the model parameters using the EM algorithm provides fundamental frequency, onset and offset time, spectral envelope and power envelope of every underlying Audio Stream. Our proposed method showed high accuracy in a pitch name estimation task of several pieces of real music performance data.

    Free Register to Access Article

  • ICASSP (3) – Audio Stream segregation of multi-pitch music signal based on time-space clustering using Gaussian kernel 2-dimensional model
    Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 1
    Co-Authors: Hirokazu Kameoka, Takuya Nishimoto, Shigeki Sagayama

    Abstract:

    The paper describes a novel approach for Audio Stream segregation of a multi-pitch music signal. We propose a parameter-constrained time-frequency spectrum model expressing both a harmonic spectral structure and a temporal curve of the power envelope with Gaussian kernels. MAP estimation of the model parameters using the EM algorithm provides fundamental frequency, onset and offset time, spectral envelope and power envelope of every underlying Audio Stream. Our proposed method showed high accuracy in a pitch name estimation task of several pieces of real music performance data.

    Free Register to Access Article