INVESTIGADORES
ALBORNOZ Enrique Marcelo
congresos y reuniones científicas
Título:
Feature set optimisation for infant cry classification
Autor/es:
VIGNOLO, LEANDRO D.; ALBORNOZ, ENRIQUE M.; MARTÍNEZ, CÉSAR E.
Lugar:
Trujillo
Reunión:
Conferencia; Ibero-American Conference on AI (IBERAMIA); 2018
Institución organizadora:
Universidad Nacional de Trujillo and the Sociedad Peruana de Inteligencia Artificial
Resumen:
This work deals with the development of features for the automatic classification of infant cry, considering three categories: neutral, fussing and crying vocalisations. Mel-frequency cepstral coefficients, together with standard functional obtained from these, have long been the most widely used features for all kind of speech-related tasks, including infant cry classification. However, recent works have introduced alternative filter banks leading to performance improvements and increasedrobustness. In this work, the optimisation of a filter bank is proposed for feature extraction and two other spectrum-based feature sets are compared. The first set of features is obtained through the optimisation of filter banks, by means of an evolutionary algorithm, in order to find a more suitable speech representation for the infant cry classification. Moreover, the classification performance of the optimised representation combined with other spectral features based on the mean log-spectrum and auditory spectrum is evaluated. The results show that these feature sets are able to improve the performance for the cry classification task.