Francesco Beritelli

A robust voice activity detector for wireless communications using soft computing

Discontinuous transmission based on speech/pause detection represents a valid solution to improve the spectral efficiency of new generation wireless communication systems. In this context, robust voice activity detection (VAD) algorithms are required, as traditional solutions present a high misclassification rate in the presence of the background noise typical of mobile environments. This paper presents a voice detection algorithm which is robust to noisy environments, thanks to a new methodology adopted for the matching process. More specifically, the VAD proposed is based on a pattern recognition approach in which the matching phase is performed by a set of six fuzzy rules, trained by means of a new hybrid learning tool. A series of objective tests performed on a large speech database, varying the signal-to-noise ratio (SNR), the types of background noise, and the input signal level, showed that, as compared with the VAD standardized by ITU-T in Recommendation G.729 annex B, the fuzzy VAD, on average, achieves an improvement in reduction both of the activity factor of about 25% and of the clipping introduced of about 43%. Informal listening tests also confirm an improvement in the perceived speech quality.

Biometric Identification Based on Frequency Analysis of Cardiac Sounds

The performance of traditional biometric identification systems is, as yet, unsatisfactory in certain applications. For this reason, other physiological or behavioral characteristics have recently been considered, using new electrical or physical signals linked to a persons vital signs. This paper examines the biometric characteristics of phonocardiogram (PCG) signals from cardiac auscultation. The idea is that PCG signals have specific individual characteristics that can be taken into consideration as a physiological sign used in a biometric system. More specifically, the paper proposes a preliminary study related to the identification of individuals via frequency analysis of cardiac sounds. The results, obtained using a database containing several heart sound recordings from 20 different people, confirm the biometric properties of PCG signals, which can thus be included among the physiological signs used by an automatic identification system.

Performance evaluation and comparison of ITU-T/ETSI voice activity detectors

The paper proposes a performance evaluation and comparison of recent ITU-T and ETSI voice activity detection algorithms. The comparison was made using both objective and psychoacoustic parameters, so as to have reliable judgements that were close to subjective ones. A highly varied speech database was also set up to evaluate the extent to which VAD depend on language, the signal to noise ratio, or the power level.

Performance analysis of an ATM multiplexer loaded with VBR traffic generated by multimode speech coders

Multimode coders are able to exploit the different characteristics of the speech waveform and to take into account the different peculiarities of background noise, thus allowing improvements in both signal reconstruction and network-offered load. In this context the variable rate code excited linear prediction (VR-CELP) coding, that is, a multimode variable bit rate (VBR) coding based on the CELP technique, has been introduced in the literature and is currently being considered for use in various applications, especially in the third-generation UMTS cellular systems. The target of the paper is to introduce an efficient and accurate framework allowing a network designer to analyze the impact of multimode VBR speech coding on the quality of service (QoS) provided by a wireless/wired ATM network. In order to capture the coder output characteristics, we propose to model a VR-CELP voice source by using a switched batch Bernoulli process (SBBP). More specifically, three models are introduced and compared in terms of accuracy and simplicity in determining network performance. As a result of the comparison, a four-state model has been chosen as the best tradeoff. The model is then used to analytically derive the loss probability and the jitter probability density function of an ATM multiplexer loaded by a number of VR-CELP sources. Finally, the proposed paradigm has been assessed in a case study where we demonstrate that, for a given output ATM link capacity and for a number of telecommunication services involving voice transmission, VR-CELP coding performs better than traditional on-off coding.

Multilevel Speech Classification Based on Fuzzy Logic

In the recent generation of v e 9 low bit-rate speech coding schemes, one of the most delicate issues is to adapt the appropriate signal excitation to the LPC filter modeling the vocal tract. The problem essentially consists of the need for a good, efficient speech pame classifier. The paper proposes a new method for multilevel speech classijcation based on Fuzzy Logic. Through simple fuzzy rules, our Fuzty Voicing Detector (FLD) system achieves a sophisticated speech classiification, returning a range of continuous values between the two extreme classes of voiced/unvoiced. As compared with traditional algorithms, the FT/2) correctly classifies typically difficult sound and, on account of its fuzzy nature, maintains good performance even in presence of hackpround noise.

Heart sounds quality analysis for automatic cardiac biometry applications

In this paper we propose a cardiac biometric system for human identity verification based on an automatic selection algorithm of the best subsequence of a DHS (Digital Heart Sound) signal. The quality score is based on the cepstral distance between homogeneous cardiac sounds. Performance of the algorithm proposed, expressed in terms of equal error rate, is similar to a DHS manual segmentation-based system, but offers the advantages of a fully automatic biometric application.

Human Identity Verification based on Heart Sounds: Recent Advances and Future Directions

Identity verification is an increasingly important process in our daily lives, and biometric recognition is a natural solution to the authentication problem. One of the most important research directions in the field of biometrics is the characterization of novel biometric traits that can be used in conjunction with other traits, to limit their shortcomings or to enhance their performance. The aim of this work is to introduce the reader to the usage of heart sounds for biometric recognition, describing the strengths and the weaknesses of this novel trait and analyzing in detail the methods developed so far by different research groups and their performance.

Human identity verification based on Mel frequency analysis of digital heart sounds

This paper presents new results in human identity verification via frequency analysis of cardiac sounds. More specifically, the paper proposes a pattern recognition approach based on a feature set of 13 Mel Frequency Cepstral Coefficients (MFCCs) extracted from the first (S1) and second (S2) heart sounds and a metric based on the power ratio of S1 to S2. The new algorithm yields significantly better performances with respect to the previous method based on the Chirp z-transform, guaranteeing an equal error rate (EER) below 9 %.

A low‐complexity speech‐pause detection algorithm for communication in noisy environments

The paper presents a new low-complexity algorithm for silence suppression in adverse acoustic environments. The algorithm uses a single time-domain input parameter (signal power) given to a simple matching block. The decision module adapts a series of thresholds depending on the current estimated signal-to-noise-ratio (SNR) of the signal. A series of tests carried out using a large speech database confirm a 10% improvement in pause detection performance as compared with the AMR VAD option 1 recently adopted by ETSI for 3rd-generation mobile systems. Copyright

Robust voiced/unvoiced speech classification using fuzzy rules

The paper presents a robust voiced/unvoiced speech classifier based on fuzzy logic. More specifically, the classification is based on a pattern recognition approach in which the matching phase is performed using a set of 5 fuzzy rules obtained by training. Certain interesting statistical properties of the fuzzy system allow the transition threshold to be adapted to the level of background noise. The results show that the performance of the fuzzy classifier in the presence of various types of background noise is better than that of traditional methods.