AI

Sound Ranking Using Auditory Sparse-Code Representations

Abstract

The task of ranking sounds from text queries is a good test application for machine-hearing techniques, and particularly for comparison and evaluation of alternative sound representations in a large-scale setting. We have adapted a machine-vision system, ``passive-aggressive model for image retrieval'' (PAMIR), which efficiently learns, using a ranking-based cost function, a linear mapping from a very large sparse feature space to a large query-term space. Using this system allows us to focus on comparison of different auditory front ends and different ways of extracting sparse features from high-dimensional auditory images. In addition to two main auditory-image models, we also include and compare a family of more conventional MFCC front ends. The experimental results show a significant advantage for the auditory models over vector-quantized MFCCs. The two auditory models tested use the adaptive pole-zero filter cascade (PZFC) auditory filterbank and sparse-code feature extraction from stabilized auditory images via multiple vector quantizers. The models differ in their implementation of the strobed temporal integration used to generate the stabilized image. Using ranking precision-at-top-k performance measures, the best results are about 70% top-1 precision and 35% average precision, using a test corpus of thousands of sound files and a query vocabulary of hundreds of words.