Background noise classification using random forest tree classifier for cochlear implant applications
Forough Saki, Nasser Kehtarnavaz · 2014
This paper presents improvements made to the previously developed noise classification path of the environment-adaptive cochlear implant speech processing pipeline. These improvements consist of the utilization of subband noise features together with a random forest tree classifier. Three commonly encountered noise environments of babble, street, and machinery are considered. The results using actual noise signals indicate that this classification method provides 10% improvement in the overall classification rate compared to the previously developed classification while maintaining the real-time implementation aspect of the entire speech processing pipeline.