A Computational Model of Auditory Feature Extraction and Sound Classification

This thesis introduces a computer model that incorporates responses similar to those found in the cochlea, in sub-corticai auditory processing, and in auditory cortex. The principle aim of this work is to show that this can form the basis for a biologically plausible mechanism of auditory stimulus classification. We will show that this classification is robust to stimulus variation and time compression. In addition, the response of the system is shown to support multiple, concurrent, behaviourally relevant classifications of natural stimuli (speech). The model incorporates transient enhancement, an ensemble of spectro - temporal filters, and a simple measure analogous to the idea of visual salience to produce a quasi-static description of the stimulus suitable either for classification with an analogue artificial neural network or, using appropriate rate coding, a classifier based on artificial spiking neurons. We also show that the spectotemporal ensemble can be derived from a limited class of 'formative' stimuli, consistent with a developmental interpretation of ensemble formation. In addition, ensembles chosen on information theoretic grounds consist of filters with relatively simple geometries, which is consistent with reports of responses in mammalian thalamus and auditory cortex. A powerful feature of this approach is that the ensemble response, from which salient auditory events are identified, amounts to stimulus-ensemble driven method of segmentation which respects the envelope of the stimulus, and leads to a quasi-static representation of auditory events which is suitable for spike rate coding. We also present evidence that the encoded auditory events may form the basis of a representation-of-similarity, or second order isomorphism, which implies a representational space that respects similarity relationships between stimuli including novel stimuli

Comparison of skewness-based salient event detector algorithms in speech

In this work, we compare two skewness-based salient event detector algorithms, which can detect transients in human speech signals. Speech transients are characterized by rapid changes in signal energy. The purpose of this study was to compare the identification of transients by two different methods based on skewness calculation in order to develop a method to be used in studying the processing of speech transients in the human brain. The first method, the skewness in variable time (SKV) finds transients using a cochlear model. The skewness of the energy distribution for a variable time window is implemented on artificial neural networks. The second method, the automatic segmentation method for transient detection (RoT) is more speech segmentation-based and developed for detecting transient speech segment ratio in spoken records. In the current study, the test corpus included Hungarian and English speech recorded from different speakers (2 male and 2 female for both languages). Results were compared by the F-measure, the Jaccard similarity index, and the Hamming distance. The results of the two algorithms were also tested against a hand-labeled corpus annotated by linguistic experts for an absolute assessment of the performance of the two methods. Transient detection was tested once for onset events alone and, separately, for onset and offset events together. The results show that in most cases, the RoT method works better on the expert labeled databases. Using F measure with +- 25ms window length the following results were obtained when all type of transient events were evaluated: 0,664 on English and 0,834 on Hungarian. Otherwise, the two methods identify the same stimulus features as the transients also coinciding with those hand-labeled by experts

A Neurocomputational Model of Stimulus-Specific Adaptation to Oddball and Markov Sequences

Stimulus-specific adaptation (SSA) occurs when the spike rate of a neuron decreases with repetitions of the same stimulus, but recovers when a different stimulus is presented. It has been suggested that SSA in single auditory neurons may provide information to change detection mechanisms evident at other scales (e.g., mismatch negativity in the event related potential), and participate in the control of attention and the formation of auditory streams. This article presents a spiking-neuron model that accounts for SSA in terms of the convergence of depressing synapses that convey feature-specific inputs. The model is anatomically plausible, comprising just a few homogeneously connected populations, and does not require organised feature maps. The model is calibrated to match the SSA measured in the cortex of the awake rat, as reported in one study. The effect of frequency separation, deviant probability, repetition rate and duration upon SSA are investigated. With the same parameter set, the model generates responses consistent with a wide range of published data obtained in other auditory regions using other stimulus configurations, such as block, sequential and random stimuli. A new stimulus paradigm is introduced, which generalises the oddball concept to Markov chains, allowing the experimenter to vary the tone probabilities and the rate of switching independently. The model predicts greater SSA for higher rates of switching. Finally, the issue of whether rarity or novelty elicits SSA is addressed by comparing the responses of the model to deviants in the context of a sequence of a single standard or many standards. The results support the view that synaptic adaptation alone can explain almost all aspects of SSA reported to date, including its purported novelty component, and that non-trivial networks of depressing synapses can intensify this novelty response

Understanding Pitch Perception as a Hierarchical Process with Top-Down Modulation

Pitch is one of the most important features of natural sounds, underlying the perception of melody in music and prosody in speech. However, the temporal dynamics of pitch processing are still poorly understood. Previous studies suggest that the auditory system uses a wide range of time scales to integrate pitch-related information and that the effective integration time is both task- and stimulus-dependent. None of the existing models of pitch processing can account for such task- and stimulus-dependent variations in processing time scales. This study presents an idealized neurocomputational model, which provides a unified account of the multiple time scales observed in pitch perception. The model is evaluated using a range of perceptual studies, which have not previously been accounted for by a single model, and new results from a neurophysiological experiment. In contrast to other approaches, the current model contains a hierarchy of integration stages and uses feedback to adapt the effective time scales of processing at each stage in response to changes in the input stimulus. The model has features in common with a hierarchical generative process and suggests a key role for efferent connections from central to sub-cortical areas in controlling the temporal dynamics of pitch processing

Snowmaking as an adaptation strategy in ski resorts - Avoiding maladaptation by a climate service

Presentation given at the UArctic Congress in Oulu, Finland on 5 September 2018 in a session on Arctic tourism and global change. The presentation is based on the WP5 CS1 team's work on co-designing a climate service for winter tourism industry in Northern Finland within the Blue-Action project (2016-2021). The Blue-Action project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No 727852