The most common types of voice disorders are associated with hyperfunctional voice use in daily life. Although current clinical practice uses measures from brief laboratory recordings to assess vocal function, it is unclear how these relate to an individual's habitual voice use. The purpose of this study was to quantify the correlation and offset between voice features computed from laboratory and ambulatory recordings in speakers with and without vocal hyperfunction. Features derived from a neck-surface accelerometer included estimates of sound pressure level, fundamental frequency, cepstral peak prominence, and spectral tilt. Whereas some measures from laboratory recordings correlated significantly with those captured during daily life, only approximately 6-52% of the actual variance was accounted for. Thus, brief voice assessments are quite limited in the extent to which they can accurately characterize the daily voice use of speakers with and without vocal hyperfunction.

Clinical diagnosis of voice disorder and evaluation of therapy outcome heavily rely on accurate quantification of voice quality, which is closely tied to the physiology and function of the laryngeal mechanism. Considering the evaluation methodology of the voice, two main categories of auditory-perceptual assessment and acoustic analysis can be identified. This article presents a new approach for acoustic analysis of voice quality, which brings several advantages to the field. The proposed approach is non-parametric in the sense that it does not require the estimation of the fundamental frequency or spectral response of the vocal tract. This reduces the computational complexity of the measurement and reduces the possible errors due to inaccurate estimation of those parameters. Additionally, the method does not make any assumption about the phonetic context and hence has the potential to be applied to connected speech. The proposed method benefits from the multiresolution structure of the wavelet analysis for estimating the noisy component of a voice in the spectro-temporal domain. The informativeness of the estimated noise for voice quality distinction is examined based on different noise-quantification approaches. It is shown that deviation from the model of the human auditory system (HAS) leads to performance improvement. Through several analyses, it is argued that using models of HAS for quantification of the noise leads to significant loss of information relevant to voice quality. Findings from this article suggest that perception-based measures of voice quality are highly restricted in capturing important aspects of acoustic that could assist with voice quality distinctions. This characteristic is inherent to HAS and cannot be alleviated, highlighting a significant limitation of perception-based measures.

Mp3 is a very popular audio format and hence it can be a good host for carrying hidden messages. Therefore, different steganography methods have been proposed for mp3 hosts, but current literature has only focused on steganalysis of mp3stego. In this paper, we mention some of the limitations of mp3stego and argue that UnderMp3Cover (Ump3c) does not have those limitations. Ump3c makes subtle changes only to the global gain of bitstream and keeps the rest of bitstream intact. Therefore, its detection is much harder than mp3stego. To address this, joint distributions between global gain and other fields of mp3 bit stream are used. The changes are detected by measuring the mutual information from those joint distributions. Furthermore, we show that different mp3 encoders have dissimilar performances. Consequently, a novel multi-layer architecture for steganalysis of Ump3c is proposed. In this manner, the first layer detects the encoder and the second layer performs the steganalysis job. One of advantages of this architecture is that feature extraction and feature selection can be optimized for each encoder separately. We show this multi-layer architecture outperforms the conventional single-layer methods. Comparing results of the proposed method with other works shows an improvement of 20.4% in the accuracy of steganalysis.

Recently, merging signal processing techniques with information security services has drawn a lot of attention. Steganography and steganalysis are among those trends. Like their counterparts in cryptology, steganography and steganalysis are in a constant battle. Steganography methods try to hide the presence of covert messages in innocuous‐looking data, whereas steganalysis methods try to break steganography algorithms and reveal the existence of hidden messages. The streaming nature of audio signals, their popularity, and their wide spread usage make them very good candidates for steganography. This has led to a very rich literature on both steganography and steganalysis of audio signals. This study intends to conduct a comprehensive review of audio steganalysis methods aggregated over near 15 years. To that end, both compressed and con‐compressed methods are reviewed, and then their important details are presented in different tables. Furthermore, some of the most recent audio steganalysis methods (both non‐compressed and compressed ones) are implemented and comparative analyses on their performances are conducted. Finally, the study provides some possible directions for future research studies on audio steganalysis.

Calibration and higher‐order statistics are standard components of image steganalysis. However, these techniques have not yet found adequate attention in audio steganalysis. Specifically, most of current studies are either non‐calibrated or only based on noise removal. The goal of this study is to fill these gaps and to show that calibrated features based on re‐embedding technique improve performance of audio steganalysis. Furthermore, the authors show that least significant bit is the most sensitive bit plane to data hiding algorithms, and therefore it can be employed as a universal embedding method. The proposed features also benefit from an efficient model which is tailored to the needs for audio steganalysis and represent the maximum deviation from human auditory system. Performance of the proposed method is evaluated on a wide range of data hiding algorithms in both targeted and universal paradigms. The results show the effectiveness of the proposed method in detecting the finest traces of data hiding algorithms in very low embedding rates. The system detects Steghide at capacity of 0.06 bit per symbol with sensitivity of 98.6% (music) and 78.5% (speech). These figures are, respectively, 7.1% and 27.5% higher than the state‐of‐the‐art results based on R‐Mel‐frequency cepstral coefficient features.