Discrimination of Speech From Non-Speech Based on Multiscale Spectro-Temporal Modulations

We describe a content-based audio classification algorithm based on novel multiscale spectrotemporal modulation features inspired by a model of auditory cortical processing. The task explored is to discriminate speech from non-speech consisting of animal vocalizations, music and environmental sounds. Although this is a relatively easy task for humans, it is still difficult to automate well, especially in noisy and reverberant environments. The auditory model captures basic processes occurring from the early cochlear stages to the central cortical areas. The model generates a multidimensional spectro-temporal representation of the sound, which is then analyzed by a multi-linear dimensionality reduction technique and classified by a Support Vector Machine (SVM). Generalization of the system to signals in high level of additive noise and reverberation is evaluated and compared to two existing approaches [1] [2]. The results demonstrate the advantages of the auditory model over the other two systems, especially at low SNRs and high reverberation

Representation of speech in the primary auditory cortex and its implications for robust speech processing

Speech has evolved as a primary form of communication between humans. This most used means of communication has been the subject of intense study for years, but there is still a lot that we do not know about it. It is an oft repeated fact, that even the performance of the best speech processing algorithms still lags far behind that of the average human, It seems inescapable that unless we know more about the way the brain performs this task, our machines can not go much further. This thesis focuses on the question of speech representation in the brain, both from a physiological and technological perspective. We explore the representation of speech through the encoding of its smallest elements - phonemic features - in the primary auditory cortex. We report on how population of neurons with diverse tuning properties respond discriminately to phonemes resulting in explicit encoding of their parameters. Next, we show that this sparse encoding of the phonemic features is a simple consequence of the linear spectro-temporal properties of the auditory cortical neurons and that a Spectro-Temporal receptive field model can predict similar patterns of activation. This is an important step toward the realization of systems that operate based on the same principles as the cortex. Using an inverse method of reconstruction, we shall also explore the extent to which phonemic features are preserved in the cortical representation of noisy speech. The results suggest that the cortical responses are more robust to noise and that the important features of phonemes are preserved in the cortical representation even in noise. Finally, we explain how a model of this cortical representation can be used for speech processing and enhancement applications to improve their robustness and performance

Scene analysis in the natural environment

The problem of scene analysis has been studied in a number of different fields over the past decades. These studies have led to a number of important insights into problems of scene analysis, but not all of these insights are widely appreciated. Despite this progress, there are also critical shortcomings in current approaches that hinder further progress. Here we take the view that scene analysis is a universal problem solved by all animals, and that we can gain new insight by studying the problems that animals face in complex natural environments. In particular, the jumping spider, songbird, echolocating bat, and electric fish, all exhibit behaviors that require robust solutions to scene analysis problems encountered in the natural environment. By examining the behaviors of these seemingly disparate animals, we emerge with a framework for studying analysis comprising four essential properties: 1) the ability to solve ill-posed problems, 2) the ability to integrate and store information across time and modality, 3) efficient recovery and representation of 3D scene structure, and 4) the use of optimal motor actions for acquiring information to progress towards behavioral goals

Single-Microphone Speech Enhancement and Separation Using Deep Learning

The cocktail party problem comprises the challenging task of understanding a speech signal in a complex acoustic environment, where multiple speakers and background noise signals simultaneously interfere with the speech signal of interest. A signal processing algorithm that can effectively increase the speech intelligibility and quality of speech signals in such complicated acoustic situations is highly desirable. Especially for applications involving mobile communication devices and hearing assistive devices. Due to the re-emergence of machine learning techniques, today, known as deep learning, the challenges involved with such algorithms might be overcome. In this PhD thesis, we study and develop deep learning-based techniques for two sub-disciplines of the cocktail party problem: single-microphone speech enhancement and single-microphone multi-talker speech separation. Specifically, we conduct in-depth empirical analysis of the generalizability capability of modern deep learning-based single-microphone speech enhancement algorithms. We show that performance of such algorithms is closely linked to the training data, and good generalizability can be achieved with carefully designed training data. Furthermore, we propose uPIT, a deep learning-based algorithm for single-microphone speech separation and we report state-of-the-art results on a speaker-independent multi-talker speech separation task. Additionally, we show that uPIT works well for joint speech separation and enhancement without explicit prior knowledge about the noise type or number of speakers. Finally, we show that deep learning-based speech enhancement algorithms designed to minimize the classical short-time spectral amplitude mean squared error leads to enhanced speech signals which are essentially optimal in terms of STOI, a state-of-the-art speech intelligibility estimator.Comment: PhD Thesis. 233 page

Exploiting primitive grouping constraints for noise robust automatic speech recognition : studies with simultaneous speech.

Significant strides have been made in the field of automatic speech recognition over the past three decades. However, the systems are not robust; their performance degrades in the presence of even moderate amounts of noise. This thesis presents an approach to developing a speech recognition system that takes inspiration firom the approach of human speech recognition

Automated Rhythmic Transformation of Drum Recordings

Within the creative industries, music information retrieval techniques are now being applied in a variety of music creation and production applications. Audio artists incorporate techniques from music informatics and machine learning (e.g., beat and metre detection) for generative content creation and manipulation systems within the music production setting. Here musicians, desiring a certain sound or aesthetic influenced by the style of artists they admire, may change or replace the rhythmic pattern and sound characteristics (i.e., timbre) of drums in their recordings with those from an idealised recording (e.g., in processes of redrumming and mashup creation). Automated transformation systems for rhythm and timbre can be powerful tools for music producers, allowing them to quickly and easily adjust the different elements of a drum recording to fit the overall style of a song. The aim of this thesis is to develop systems for automated transformation of rhythmic patterns of drum recordings using a subset of techniques from deep learning called deep generative models (DGM) for neural audio synthesis. DGMs such as autoencoders and generative adversarial networks have been shown to be effective for transforming musical signals in a variety of genres as well as for learning the underlying structure of datasets for generation of new audio examples. To this end, modular deep learning-based systems are presented in this thesis with evaluations which measure the extent of the rhythmic modifications generated by different modes of transformation, which include audio style transfer, drum translation and latent space manipulation. The evaluation results underscore both the strengths and constraints of DGMs for transformation of rhythmic patterns as well as neural synthesis of drum sounds within a variety of musical genres. New audio style transfer (AST) functions were specifically designed for mashup-oriented drum recording transformation. The designed loss objectives lowered the computational demands of the AST algorithm and offered rhythmic transformation capabilities which adhere to a larger rhythmic structure of the input to generate music that is both creative and realistic. To extend the transformation possibilities of DGMs, systems based on adversarial autoencoders (AAE) were proposed for drum translation and continuous rhythmic transformation of bar-length patterns. The evaluations which investigated the lower dimensional representations of the latent space of the proposed system based on AAEs with a Gaussian mixture prior (AAE-GM) highlighted the importance of the structure of the disentangled latent distributions of AAE-GM. Furthermore, the proposed system demonstrated improved performance, as evidenced by higher reconstruction metrics, when compared to traditional autoencoder models. This implies that the system can more accurately recreate complex drum sounds, ensuring that the produced rhythmic transformation maintains richness of the source material. For music producers, this means heightened fidelity in drum synthesis and the potential for more expressive and varied drum tracks, enhancing the creativity in music production. This work also enhances neural drum synthesis by introducing a new, diverse dataset of kick, snare, and hi-hat drum samples, along with multiple drum loop datasets for model training and evaluation. Overall, the work in this thesis increased the profile of the field and hopefully will attract more attention and resources to the area, which will help drive future research and development of neural rhythmic transformation systems

Content-based music structure analysis

Ph.DDOCTOR OF PHILOSOPH

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies