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An acoustic echo canceller optimized for hands-free speech telecommunication in large vehicle cabins
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. Department of Electronics, and Connected Services (EACS), Volvo Technology, Göteborg, Sweden.
Department of Statistics and Machine Learning, Linköping University, Linköping, Sweden.
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.ORCID iD: 0000-0003-4228-2774
2023 (English)In: EURASIP Journal on Audio, Speech, and Music Processing, ISSN 1687-4714, E-ISSN 1687-4722, Vol. 2023, no 1, article id 39Article in journal (Refereed) Published
Abstract [en]

Acoustic echo cancelation (AEC) is a system identification problem that has been addressed by various techniques and most commonly by normalized least mean square (NLMS) adaptive algorithms. However, performing a successful AEC in large commercial vehicles has proved complicated due to the size and challenging variations in the acoustic characteristics of their cabins. Here, we present a wideband fully linear time domain NLMS algorithm for AEC that is enhanced by a statistical double-talk detector (DTD) and a voice activity detector (VAD). The proposed solution was tested in four main Volvo truck models, with various cabin geometries, using standard Swedish hearing-in-noise (HINT) sentences in the presence and absence of engine noise. The results show that the proposed solution achieves a high echo return loss enhancement (ERLE) of at least 25 dB with a fast convergence time, fulfilling ITU G.168 requirements. The presented solution was particularly developed to provide a practical compromise between accuracy and computational cost to allow its real-time implementation on commercial digital signal processors (DSPs). A real-time implementation of the solution was coded in C on an ARM Cortex M-7 DSP. The algorithmic latency was measured at less than 26 ms for processing each 50-ms buffer indicating the computational feasibility of the proposed solution for real-time implementation on common DSPs and embedded systems with limited computational and memory resources. MATLAB source codes and related audio files are made available online for reference and further development.

Place, publisher, year, edition, pages
Springer, 2023. Vol. 2023, no 1, article id 39
Keywords [en]
Acoustic echo cancelation, Adaptive filters, Automotive speech processing, Automotive voice assistant, Hands-free telephony, Keyword spotting, NLMS, Speech signal enhancement
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:umu:diva-215398DOI: 10.1186/s13636-023-00305-7Scopus ID: 2-s2.0-85173557384OAI: oai:DiVA.org:umu-215398DiVA, id: diva2:1807749
Available from: 2023-10-27 Created: 2023-10-27 Last updated: 2023-10-27Bibliographically approved

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Saremi, AminGhaffari, GhazalehGu, Zonghua

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CiteExportLink to record
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Citation style
  • apa
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