The lasso and elastic-net algorithms for predictive sound quality models using large pool of predictive metrics and factors


  • Abdelghani Benghanem Université de Sherbrooke
  • Philippe-Aubert Gauthier <p>Université de Sherbrooke</p>
  • Lévy Leblanc Université de Sherbrooke
  • Alain Berry Université de Sherbrooke


Part of the overall perceived quality is communicated by sound quality. Sound quality studies present a twofold challenge. The first is that they rely on listening tests. However, listening tests are considered time consuming. The second challenge relates to the development of predictive sound quality models. Often, these models are derived using linear regression on a limited set of predictive metrics. However, nowadays, many potential metrics are available and there is no computational burden that should limit the number of potential metrics. An issue is that regression using more metrics than observations cannot lead to meaningful predictive models since all the metrics are selected. In this paper, different algorithms are compared to construct a sound quality predictive model that does not suffer from these limitations. These algorithms achieve an automatic selection of few metrics from a large pool. The lasso, elastic-net and stepwise algorithms are tested for the prediction of listening tests results of consumer product for which more than 100 metrics are used a potential predictors. It is shown that the most promising algorithm is the lasso which is able to efficiently limit the number of metrics and provide a model that can be used as understandable design guidelines.

Author Biographies

Abdelghani Benghanem, Université de Sherbrooke

Sherbrooke, Quebec 

Philippe-Aubert Gauthier, <p>Université de Sherbrooke</p>

Sherbrooke, Quebec

Lévy Leblanc, Université de Sherbrooke

Sherbrooke, Quebec

Alain Berry, Université de Sherbrooke

Sherbrooke, Quebec




How to Cite

Benghanem A, Gauthier P-A, Leblanc L, Berry A. The lasso and elastic-net algorithms for predictive sound quality models using large pool of predictive metrics and factors. Canadian Acoustics [Internet]. 2016 Aug. 24 [cited 2021 Dec. 3];44(3). Available from:



Proceedings of the Acoustics Week in Canada

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