Speech-in-noise enhancement using amplification and dynamic range compression controlled by the speech intelligibility index
- PMID: 26627746
- DOI: 10.1121/1.4932168
Speech-in-noise enhancement using amplification and dynamic range compression controlled by the speech intelligibility index
Abstract
In many speech communication applications, such as public address systems, speech is degraded by additive noise, leading to reduced speech intelligibility. In this paper a pre-processing algorithm is proposed that is capable of increasing speech intelligibility under an equal-power constraint. The proposed AdaptDRC algorithm comprises two time- and frequency-dependent stages, i.e., an amplification stage and a dynamic range compression stage that are both dependent on the Speech Intelligibility Index (SII). Experiments using two objective measures, namely, the extended SII and the short-time objective intelligibility measure (STOI), and a formal listening test were conducted to compare the AdaptDRC algorithm with a modified version of a recently proposed algorithm in three different noise conditions (stationary car noise and speech-shaped noise and non-stationary cafeteria noise). While the objective measures indicate a similar performance for both algorithms, results from the formal listening test indicate that for the two stationary noises both algorithms lead to statistically significant improvements in speech intelligibility and for the non-stationary cafeteria noise only the proposed AdaptDRC algorithm leads to statistically significant improvements. A comparison of both objective measures and results from the listening test shows high correlations, although, in general, the performance of both algorithms is overestimated.
Similar articles
-
Evaluation of the sparse coding shrinkage noise reduction algorithm in normal hearing and hearing impaired listeners.Hear Res. 2014 Apr;310:36-47. doi: 10.1016/j.heares.2014.01.006. Epub 2014 Feb 2. Hear Res. 2014. PMID: 24495441
-
Comparing Binaural Pre-processing Strategies I: Instrumental Evaluation.Trends Hear. 2015 Dec 30;19:2331216515617916. doi: 10.1177/2331216515617916. Trends Hear. 2015. PMID: 26721920 Free PMC article.
-
Effects of noise suppression on intelligibility. II: An attempt to validate physical metrics.J Acoust Soc Am. 2014 Jan;135(1):439-50. doi: 10.1121/1.4837238. J Acoust Soc Am. 2014. PMID: 24437784
-
Measuring up to speech intelligibility.Int J Lang Commun Disord. 2013 Nov-Dec;48(6):601-12. doi: 10.1111/1460-6984.12061. Epub 2013 Oct 10. Int J Lang Commun Disord. 2013. PMID: 24119170 Review.
-
Noise profiling for speech enhancement employing machine learning models.J Acoust Soc Am. 2022 Dec;152(6):3595. doi: 10.1121/10.0016495. J Acoust Soc Am. 2022. PMID: 36586827 Review.
Cited by
-
Speech Intelligibility Prediction using Spectro-Temporal Modulation Analysis.IEEE/ACM Trans Audio Speech Lang Process. 2021;29:210-225. doi: 10.1109/taslp.2020.3039929. Epub 2020 Nov 24. IEEE/ACM Trans Audio Speech Lang Process. 2021. PMID: 33748329 Free PMC article.
-
Acoustic Sensing Analytics Applied to Speech in Reverberation Conditions.Sensors (Basel). 2021 Sep 21;21(18):6320. doi: 10.3390/s21186320. Sensors (Basel). 2021. PMID: 34577527 Free PMC article.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources