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Comparison of Supervised and Semi-supervised Beamformers Using Real Audio Recordings

Authors:
Florian Heese, Magnus Schäfer, Peter Vary, Elior Hadad, Shmulik Markovich Golan, and Sharon Gannot
Book Title:
Proceedings of IEEE 27-th Convention of Electrical and Electronics Engineers in Israel (IEEEI)
Venue:
Eilat, Israel
Event Date:
14.-17.11.2012
Organization:
IEEE
Date:
Nov. 2012
Pages:
1–5
ISBN:
978-1-46734-681-8
URL:
10.1109/EEEI.2012.6376965
Language:
English

Abstract

In this contribution two different disciplines for designing microphone array beamformers are explored. On the one hand a fixed beamformer based on numerical near field opti- mization is employed. On the other hand an adaptive beamformer algorithm based on the linearly constrained minimum variance (LCMV) method is applied. For the evaluation, an audio-database for microphone array impulse responses and audio recordings (speech and noise) was created. Different acoustic scenarios were constructed, consisting of various audio sources (desired speaker, interfering speaker and directional noise) distributed around the microphone array at different angles and distances. The algorithms were compared based on both objective measure (signal-to-noise, signal-to-interference and speech distortion, and subjective tests (assessment of sonograms and informal listening tests).