The 29th AES Conference, "Audio for Mobile and Handheld Devices", all digital, September 2 to 4, 2006 Seoul National, lUniversity Campus
Digital Audio Presentations - Part 2
Pre-processing Method For Enhancing Digital Audio Quality In Speech Communication System
Geun-Bae Song, Chul-Yong Ahn, Jae Bum Kim and Austin Kim, Samsung Electronics, Korea
Ho-Chong Park, Kwangwoon University, Korea
This paper presents a preprocessing method to modify the input digital audio signals of a speech coder to obtain the finally enhanced signals at the decoder. For the purpose, we introduce the noise suppression (NS) scheme and the adaptive gain control (AGC) where an audio input and its coding error are considered as a noisy signal and a noise, respectively. The coding error is suppressed from the input and then the suppressed input is level aligned to the original input by the following AGC operation. Consequently, this preprocessing method makes the spectral energy of the music input redistributed all over the spectral domain so that the preprocessed music can be coded more effectively by the following coder. As an artifact, this procedure needs an additional encoding pass to calculate the coding error. However, it provides a generalized formulation applicable to a lot of existing speech coders. By preference listening tests, it was indicated that the proposed approach produces significant enhancements in the perceived digital music qualities.
Automatic Commercial Monitoring for TV Broadcasting Using Audio Fingerprinting
Dalwon Jang, Sunil Lee1 and Chang D. Yoo, Korea Advanced Institute of Science and Technology, Korea,
Seungjae Lee, Jun Seok Lee, Minho Jin and Jin S. Seo, Electronics and Telecommunications Research Institute, Korea
In this paper, an automatic digital commercial monitoring system using audio fingerprinting is proposed. The goal of the commercial monitoring system is to identify the title and the exact duration of commercials in real-time. To achieve this, only the audio is considered. The audio is easy to handle in real-time and can provide high accuracy for commercial identification. More precisely, the spectral subband centroids are extracted from an audio part of a commercial and indexed using the K-D tree algorithm. To detect aired commercials robustly, a four-step verification method using the indexed tree of the commercials is proposed. Experimental results show that the proposed system is robust against degradations during the real digital broadcasting and recording process and thus can fulfil commercial monitoring satisfactorily.