Abstract
The benefit of auditory models for solving three music recognition tasks—onset detection, pitch estimation, and instrument recognition—is analyzed. Appropriate features are introduced which enable the use of supervised classification. The auditory model-based approaches are tested in a comprehensive study and compared to state-of-the-art methods, which usually do not employ an auditory model. For this study, music data is selected according to an experimental design, which enables statements about performance differences with respect to specific music characteristics. The results confirm that the performance of music classification using the auditory model is comparable to the traditional methods. Furthermore, the auditory model is modified to exemplify the decrease of recognition rates in the presence of hearing deficits. The resulting system is a basis for estimating the intelligibility of music which in the future might be used for the automatic assessment of hearing instruments.
Highlights
Hearing-impaired listeners like to enjoy music as well as normal-hearing listeners this is impeded by a distorted perception of music signals
6.1 Comparison of proposed approaches We will look at the results of onset detection, pitch estimation, and instrument recognition, consecutively
6.1.1 Onset detection Table 6 shows the results of onset detection for the three considered approaches: (1) common onset detection on the original signal, Table 6 Results for onset detection with and without an auditory model (AM)
Summary
Hearing-impaired listeners like to enjoy music as well as normal-hearing listeners this is impeded by a distorted perception of music signals. We investigate the potential of emulating certain normal-hearing and hearing-impaired listeners by automatically assessing their ability to discriminate music attributes via an auditory model. Auditory models are computational models which mimic the human auditory process by transforming acoustic signals into neural activity of simulated auditory nerve fibers (channels). Since these models do not explain the whole listening comprehension of higher central auditory stages, a back end is needed relying on the output of the auditory periphery.
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