Learning Audio Sequence Representations for Acoustic Event Classification

Acoustic Event Classification (AEC) has become a significant task for machines to perceive the surrounding auditory scene. However, extracting effective representations that capture the underlying characteristics of the acoustic events is still challenging. Previous methods mainly focused on designing the audio features in a 'hand-crafted' manner. Interestingly, data-learnt features have been recently reported to show better performance. Up to now, these were only considered on the frame-level. In this paper, we propose an unsupervised learning framework to learn a vector representation of an audio sequence for AEC. This framework consists of a Recurrent Neural Network (RNN) encoder and a RNN decoder, which respectively transforms the variable-length audio sequence into a fixed-length vector and reconstructs the input sequence on the generated vector. After training the encoder-decoder, we feed the audio sequences to the encoder and then take the learnt vectors as the audio sequence representations. Compared with previous methods, the proposed method can not only deal with the problem of arbitrary-lengths of audio streams, but also learn the salient information of the sequence. Extensive evaluation on a large-size acoustic event database is performed, and the empirical results demonstrate that the learnt audio sequence representation yields a significant performance improvement by a large margin compared with other state-of-the-art hand-crafted sequence features for AEC

Idealized computational models for auditory receptive fields

This paper presents a theory by which idealized models of auditory receptive fields can be derived in a principled axiomatic manner, from a set of structural properties to enable invariance of receptive field responses under natural sound transformations and ensure internal consistency between spectro-temporal receptive fields at different temporal and spectral scales. For defining a time-frequency transformation of a purely temporal sound signal, it is shown that the framework allows for a new way of deriving the Gabor and Gammatone filters as well as a novel family of generalized Gammatone filters, with additional degrees of freedom to obtain different trade-offs between the spectral selectivity and the temporal delay of time-causal temporal window functions. When applied to the definition of a second-layer of receptive fields from a spectrogram, it is shown that the framework leads to two canonical families of spectro-temporal receptive fields, in terms of spectro-temporal derivatives of either spectro-temporal Gaussian kernels for non-causal time or the combination of a time-causal generalized Gammatone filter over the temporal domain and a Gaussian filter over the logspectral domain. For each filter family, the spectro-temporal receptive fields can be either separable over the time-frequency domain or be adapted to local glissando transformations that represent variations in logarithmic frequencies over time. Within each domain of either non-causal or time-causal time, these receptive field families are derived by uniqueness from the assumptions. It is demonstrated how the presented framework allows for computation of basic auditory features for audio processing and that it leads to predictions about auditory receptive fields with good qualitative similarity to biological receptive fields measured in the inferior colliculus (ICC) and primary auditory cortex (A1) of mammals.Comment: 55 pages, 22 figures, 3 table

Sound Object Recognition

Humans are constantly exposed to a variety of acoustic stimuli ranging from music and speech to more complex acoustic scenes like a noisy marketplace. The human auditory perception mechanism is able to analyze these different kinds of sounds and extract meaningful information suggesting that the same processing mechanism is capable of representing different sound classes. In this thesis, we test this hypothesis by proposing a high dimensional sound object representation framework, that captures the various modulations of sound by performing a multi-resolution mapping. We then show that this model is able to capture a wide variety of sound classes (speech, music, soundscapes) by applying it to the tasks of speech recognition, speaker verification, musical instrument recognition and acoustic soundscape recognition. We propose a multi-resolution analysis approach that captures the detailed variations in the spectral characterists as a basis for recognizing sound objects. We then show how such a system can be fine tuned to capture both the message information (speech content) and the messenger information (speaker identity). This system is shown to outperform state-of-art system for noise robustness at both automatic speech recognition and speaker verification tasks. The proposed analysis scheme with the included ability to analyze temporal modulations was used to capture musical sound objects. We showed that using a model of cortical processing, we were able to accurately replicate the human perceptual similarity judgments and also were able to get a good classification performance on a large set of musical instruments. We also show that neither just the spectral feature or the marginals of the proposed model are sufficient to capture human perception. Moreover, we were able to extend this model to continuous musical recordings by proposing a new method to extract notes from the recordings. Complex acoustic scenes like a sports stadium have multiple sources producing sounds at the same time. We show that the proposed representation scheme can not only capture these complex acoustic scenes, but provides a flexible mechanism to adapt to target sources of interest. The human auditory perception system is known to be a complex system where there are both bottom-up analysis pathways and top-down feedback mechanisms. The top-down feedback enhances the output of the bottom-up system to better realize the target sounds. In this thesis we propose an implementation of top-down attention module which is complimentary to the high dimensional acoustic feature extraction mechanism. This attention module is a distributed system operating at multiple stages of representation, effectively acting as a retuning mechanism, that adapts the same system to different tasks. We showed that such an adaptation mechanism is able to tremendously improve the performance of the system at detecting the target source in the presence of various distracting background sources