Object Tracking from Audio and Video data using Linear Prediction method

Microphone arrays and video surveillance by camera are widely used for detection and tracking of a moving speaker. In this project, object tracking was planned using multimodal fusion i.e., Audio-Visual perception. Source localisation can be done by GCC-PHAT, GCC-ML for time delay estimation delay estimation. These methods are based on spectral content of the speech signals that can be effected by noise and reverberation. Video tracking can be done using Kalman filter or Particle filter. Therefore Linear Prediction method is used for audio and video tracking. Linear prediction in source localisation use features related to excitation source information of speech which are less effected by noise. Hence by using this excitation source information, time delays are estimated and the results are compared with GCC PHAT method. The dataset obtained from [20] is used in video tracking a single moving object captured through stationary camera. Then for object detection, projection histogram is done followed by linear prediction for tracking and the corresponding results are compared with Kalman filter method

Bio-inspired broad-class phonetic labelling

Recent studies have shown that the correct labeling of phonetic classes may help current Automatic Speech Recognition (ASR) when combined with classical parsing automata based on Hidden Markov Models (HMM).Through the present paper a method for Phonetic Class Labeling (PCL) based on bio-inspired speech processing is described. The methodology is based in the automatic detection of formants and formant trajectories after a careful separation of the vocal and glottal components of speech and in the operation of CF (Characteristic Frequency) neurons in the cochlear nucleus and cortical complex of the human auditory apparatus. Examples of phonetic class labeling are given and the applicability of the method to Speech Processing is discussed

Speaker Normalization Using Cortical Strip Maps: A Neural Model for Steady State vowel Categorization

Auditory signals of speech are speaker-dependent, but representations of language meaning are speaker-independent. The transformation from speaker-dependent to speaker-independent language representations enables speech to be learned and understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitch-independent representation of speech sounds, while also preserving information about speaker identity. This speaker-invariant representation is categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be categorized, or chunked, into syllable and word representations. The proposed model fits into an emerging model of auditory streaming and speech categorization. The auditory streaming and speaker normalization parts of the model both use multiple strip representations and asymmetric competitive circuits, thereby suggesting that these two circuits arose from similar neural designs. The normalized speech items are rapidly categorized and stably remembered by Adaptive Resonance Theory circuits. Simulations use synthesized steady-state vowels from the Peterson and Barney [J. Acoust. Soc. Am. 24, 175-184 (1952)] vowel database and achieve accuracy rates similar to those achieved by human listeners. These results are compared to behavioral data and other speaker normalization models.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Application of Swept-Sine Excitation for Acoustic Impedance Education

The NASA Langley Normal Incidence Tube (NIT) and Grazing Flow Impedance Tube (GFIT) are regularly employed to characterize the frequency response of acoustic liners through the eduction of their specific acoustic impedance. Both test rigs typically use an acoustic source that produces sine wave signals at discrete frequencies (Stepped-Sine) to educe the impedance. The current work details a novel approach using frequency-swept sine waveforms normalized to a constant sound pressure level for excitation. Determination of the sound pressure level and phase from microphone measurements acquired using swept-sine excitation is performed using a modified Vold-Kalman order tracking filter. Four acoustic liners are evaluated in the NIT and GFIT with both stepped-sine and swept-sine sources. Using these two methods, the educed impedance spectra are shown to compare favorably. However, the new (Swept-Sine) approach provides much greater frequency resolution in less time, allowing the acoustic liner properties to be studied in much greater detail