This paper investigates acoustic modeling for recognition of bird species from audio field recordings. First, the acoustic scene is decomposed into isolated segments, corresponding to detected sinusoids. Each segment is represented by a sequence of the frequency and normalized magnitude values of the sinusoid. The temporal evolution of these features is modeled using hidden Markov models (HMMs). A novel method for an unsupervised modeling of individual bird vocalization elements is proposed. The element models are initialized using HMM-based clustering and then further trained using an iterative maximum likelihood label re-assignment procedure. State duration modeling, performed in a post-recognition stage, is explored. Finally, we developed a hybrid deep neural network—hidden Markov model. The developed acoustic models are employed for bird species identification, detection of specific species, and recognition of multiple bird species vocalizing in a given recording. The detection system employs score normalization. Recognition of multiple bird species is performed based on maximizing the likelihood of a set of segments on a subset of bird species models, with penalization based on Bayesian information criterion applied. Experimental evaluations are performed on more than 37 h of sound field recordings, containing vocalizations of 48 bird species, plus more than 16 h of non-bird sound recordings. Using 3 s of the detected signal, the best system achieved: identification accuracy of 98.7%, detection with the equal error rate of 2.7%, and recognition accuracy of 97.3% and 95.4% when vocalizations of multiple bird species are present, with the number of bird species known and estimated, respectively.

This paper presents an HMM-based automatic system for recognition of bird species from audio field recordings. It includes an improved unsupervised modelling of individual bird syllables and duration modelling. The acoustic signal is decomposed into isolated segments, each segment containing a temporal evolution of a detected sinusodal component. Modelling of bird syllables is performed using Hidden Markov models (HMMs). A set of syllables of bird vocalisations is discovered in an unsupervised manner by employing dynamic time warping and agglomerative hierarchical clustering. A novel iterative maximum likelihood procedure is used to train individual HMMs for syllables of each species. Modelling of the state duration is employed in a post-recognition stage by combining the likelihood of the acoustic and duration modelling. Experiments are performed on over 33 hours of field recordings, containing 30 bird species. Evaluations demonstrate that the use of the proposed un-supervised iterative HMM training procedure and the duration modelling provides in average 45% error rate reduction. The presented system recognises bird species with accuracy of 97.8% using 3 seconds of the detected signal.

This paper presents an extension of our recent work on recognition of multiple bird species from their vocalisations by incorporating an improved acoustic modelling. The acoustic scene is segmented into spectro-temporal isolated segments by employing a sinusoidal detection algorithm, which is able to handle multiple simultaneous bird vocalisations. Each segment is represented as a temporal sequence of frequencies of the detected sinusoid. Each bird species is represented by a set of hidden Markov models (HMMs), each HMM modelling a particular vocalisation element. A set of elements is discovered in an unsupervised manner using a partial dynamic time warping algorithm and agglomerative hierarchical clustering. Recognition of multiple bird species is performed based on maximising the likelihood of the set of detected segments on a subset of bird species models, with a penalisation applied for increasing the number of bird species. Experimental evaluations used audio field recordings containing 30 bird species. Detected segments from several bird species are joined to simulate the presence of multiple bird species. It is demonstrated that the use of improved acoustic modelling in conjunction with the maximum likelihood score combination method provides considerable improvements over previous results and the use of majority voting.

This paper presents an automatic system for recognition of bird species from audio field recordings. The acoustic signal is first segmented into isolated time-frequency segments, each corresponding to an individual detected sinusoidal component. Each segment is represented by a temporal sequence of the frequency values of the detected sinusoid, referred to as frequency track. Hidden Markov models (HMMs) are employed to model the temporal evolution of frequency track features. Individual syllables of bird vocalisations are discovered using an unsupervised method based on dynamic time warping and agglomerative hierarchical clustering. The outcome of this is then employed to create individual HMMs for syllables of each species. Experiments are performed on over 33 hours of field recordings, containing 30 bird species. Evaluations demonstrate that the use of individual syllable HMMs provides over 40% error rate reduction over the use of single HMM for each bird species of the same complexity. The syllable HMM-based system recognises bird species with accuracy over 95% using 3 seconds of detected signal.

Alarm sounds triggered by biomedical equipment play a key role in providing healthcare in a neonatal intensive care unit (NICU). This paper presents our work on automatic detection of acoustic alarms in a noisy NICU environment, where knowledge about the particular characteristics of each alarm class is integrated at different stages of the detection system. The feature extraction is based on applying, around alarm-specific frequencies, a method for detection of sinusoidal signals, which employs the normalised short-term magnitude and phase spectrum. Also, the ratios of magnitudes at those frequencies are\ntaken as features. The system consists of a set of GMM-based detectors, each designed to deal with a specific alarm. Temporal structure of alarms, in terms of duration of signal and silence intervals in every alarm period, is incorporated by aggregating the frame-level posterior probabilities. The experimental evaluations are performed with a database recorded in a real-world hospital environment. The performance of the detection system is assessed both at the frame level and at the alarm period level.

Automatic system for recognition of multiple bird species in audio recordings is presented. Time-frequency segmentation of the acoustic scene is obtained by employing a sinusoidal detection algorithm, which does not require any estimate of noise and is able to handle multiple simultaneous bird vocalizations. Each segment is characterized as a sequence of frequencies over time, referred to as a frequency track. Each bird species is represented by a hidden Markov model that models the temporal evolution of frequency tracks. The decision on the number and identity of bird species in a given recording is obtained based on maximizing the overall likelihood of the set of detected segments, with a penalization applied for increasing the number of bird models used. Experimental evaluations are performed on audio field recordings containing 30 bird species. The presence of multiple bird species is simulated by joining the set of detected segments from several bird species. Results show that the proposed method can achieve recognition performance for multiple bird species not far from that obtained for single bird species, and considerably outperforms majority voting methods.

This paper presents an automatic system for recognition of bird species from field audio recordings. The proposed system employs a novel method for detection of sinusoidal components in the acoustic scene. This provides a segmentation of the signal and also feature representation of each segment in terms of frequencies over time, referred to as frequency track. We employ hidden Markov models (HMMs) to model the temporal evolution of frequency tracks. We demonstrate the effect of including local temporal dynamics of frequency tracks and HMM modelling parameters. Experiments are performed on over 33 hours of field recordings, containing 30 bird species. Evaluations demonstrate that the HMM-based temporal modelling provides considerable performance improvement over a system based on Gaussian mixture modelling. The proposed HMM-based system is capable of recognising bird species with accuracy over 85% from only 3 seconds of detected signal.

This paper presents a method for detection of sinusoidal signals corrupted by an additive noise in the short-time Fourier domain. The proposed method is based on probabilistic modelling of the spectral magnitude shape and phase continuity around spectral peaks and can deal with both stationary and non-stationary sinusoidal signals. Experimental results are presented for both sinusoidal signals of a constant frequency and frequency varying continuously over time. The performance is analysed in terms of the false acceptance and false rejection error rates of spectral peak detection and also compared to our previous method. Experimental results demonstrate very high detection accuracy in even very strong noisy conditions.

In this paper we investigate an incorporation of mask modelling into an HMM-based ASR system. The mask model is estimated for each HMM state and mixture by using a separate Viterbi-style training procedure and it expresses which regions of the spectrum are expected to be uncorrupted by noise for the HMM state. Experimental evaluation is performed on noisy speech data from the Aurora 2 database. Significant performance improvements are achieved when the mask modelling is incorporated within the standard model and two models that had already compensated for the effect of the noise.

In this paper, we propose a novel model for incorporating the voicing information in a speech recognition system. The voicing information employed is estimated by a novel method that can provide this information for each filter-bank channel, without requiring any information about the fundamental frequency. A Viterbi-style training procedure is employed to estimate the voicing-probability of each mixture at each HMM state. Experiments are performed on noisy speech data from the Aurora 2 database. Significant performance improvements are achieved at low SNRs when the voicing information is incorporated within the standard model and two models that had already compensated for the effect of the noise.

This paper proposes a novel MAP denoising algorithm that uses the ICA transformation and provide a derivation demonstrating the type of situations in which the use of ICA transformation is expected to achieve best results. We also propose an employment of generalized Gaussian model (GGM) for modelling the speech and noise distributions. The performance of the proposed speech enhancement algorithm is compared with the Wiener filtering and sparse code shrinkage method. The experiments are focused on speech signal corrupted by a non-Gaussian noise. The experimental results show that the proposed algorithm achieves significantly better performance than both the Wiener filtering and the sparse code shrinkage method.

This letter presents a method for estimation of the voicing-character of speech spectra. It is based on a calculation of a similarity between the shape of the signal short-term magnitude spectra and spectra of the frame-analysis window, which is weighted by the signal magnitude spectra. It is demonstrated that the proposed voicing measure is related to the local SNR of noise-corrupted voiced speech. The performance is evaluated for detection of voiced regions in the spectra of speech corrupted by various types of noise. The experimental results in terms of false-acceptance and false-rejection show errors of less than 5% for speech corrupted by white noise at the local SNR of 10 dB and in terms of recognition accuracy obtained by an ASR system using the voicing information estimated by the proposed method and by the fulla prioriknowledge about the noise show similar recognition performance.

This paper presents a novel method for estimating the voicing-character of speech spectra, demonstrates its employment in noise robust ASR and proposes a modified calculation of filter-bank energies. The proposed voicing-character estimation is based on calculation of a similarity between the shape of the signal short-term magnitude spectra around spectral peaks and spectra of the frame-analysis window. The similarity is weighted by the signal magnitude spectra to reflect the filter-bank analysis typically used in feature extraction for speech recognition. The experimental results show less than 5% false-acceptance and false-rejection errors in detection of voiced filter-bank channels in speech signal corrupted by white noise at 10 dB local SNR. The recognition results obtained by a missing-feature based ASR system using features estimated as voiced by the proposed method are very similar to using oracle voicing-label obtained by full a-priori knowledge of noise. The employment of features obtained by a modified calculation of filter-bank energies shows further improvements in the recognition accuracy

Colorectal cancer (CRC) is one of the most common fatal cancers in developed countries and represents a significant public-health issue. About 3%-5% of patients with CRC have hereditary nonpolyposis colorectal cancer (HNPCC). Cancer morbidity and mortality can be reduced if early and intensive screening is pursued. However, despite advances in screening, population-wide genetic screening for HNPCC is not currently considered feasible due to its complexity and expense. If the risk of a family having HNPCC can be identified/assessed, then only the high-risk fraction of the population would undergo intensive screening. This identification is currently performed by a genetic counselor/physician who makes the decision based on some pre-defined criteria. Here, we report on a system to identify the risk of a family having HNPCC based on its history. We compare artificial neural networks and statistical approaches for assessing the risk of a family having HNPCC and discuss the experimental results obtained by these two approaches.

This paper is concerned with the multi-stream approach in speech recognition. In a given set of feature streams, there may be some features corrupted by noise. Ideally, these features should be excluded from recognition. To achieve this, a-priori knowledge about the identity, including both the number and location, of the noisy features is required. We present a method for estimating the number of noisy feature streams. This method assumes no knowledge about the noise. It is based on calculation of the reliability of each feature stream and then evaluation of the joint maximal reliability. Since this method decreases the uncertainty about the noisy features and is statistical in nature, it can also be used to increase robustness of other classification systems. We present an application of this method to model-order selection in the union models. We performed tests on the TIDIGITS database, corrupted by noises affecting various numbers of feature streams. The experimental results show that this model achieves recognition performance similar to the one obtained with a-priori knowledge about the identity of the corrupted features.

This paper describes a prototype model-based codec capable of encoding head-and-torso imagery, which shows potential as a communication aid for the deaf. It allows facial motion to be transferred sufficiently accurately that lip-reading may be supported at data rates well under 64 kbits/s.

A simple approach to model-based coding has been developed. This uses 2D feature identification and tracking, and infers 3D motion by a rule-based approach. The technique is described and the result of applying it to human image sequences discussed.>