Objective: This study was a basic investigation of the ability of concurrent speech segregation in hearing impaired children. Concurrent segregation is one of the fundamental components of auditory scene analysis and plays an important role in process of speech perception. In the present study, auditory late potentials of impaired children were studied and were compared to the responses of normal groups.Materials & Methods: Auditory late potentials in response to 12 double vowels were recorded in 10 children with moderate to severe sensory neural hearing loss and 10 normal children who were selected randomly. The age range of all participants was 4-6 years old (4.95 ± 0.72 for the normal group and 5.05 ± 0.79 for the hearing impaired ones). Double vowels (pairs of synthetic vowels) were presented concurrently and binaurally. Fundamental frequency (F0) of these vowels and the size of the difference in F0 between vowels was 100 Hz and 0.5 semitones respectively. The hearing impaired children were selected randomly and blindly.Results: Comparing N1-P2 amplitude showed statistically significant difference in some stimuli between hearing impaired and normal children (P-value<0.05). More careful study of the results, N1-P2 amplitude- which is index of detecting vowel changes and reflecting verbal stimuli in central auditory system in absence of active participants- decreased in hearing impaired children.Conclusion: The present study showed concurrent speech segregation problems in hearing impaired children and provides evidence on problems in detection of speech features in a bo􀆩om-up processing (based on fundamental frequency and its differences) in the mentioned children.

Background: Auditory scene analysis (ASA) is the process by which the auditory system separates individual sounds in natural-world situations. ASA is a key function of auditory system, and contributes to speech discrimination in noisy backgrounds. It is known that sensorineural hearing loss (SNHL) detrimentally affects auditory function in complex environments, but relatively few studies have focused on the influence of SNHL on higher level processes which are likely involved in auditory perception in different situations.Objectives: The purpose of the current study was to compare the auditory system ability of normally hearing and SNHL subjects using the ASA examination.Materials and Methods: A total of 40 right-handed adults (age range: 18-45 years) participated in this study. The listeners were divided equally into control and mild to moderate SNHL groups. ASA ability was measured using an ABA-ABA sequence. The frequency of the "A" was kept constant at 500, 1000, 2000 or 4000 Hz, while the frequency of the "B" was set at 3 to 80 percent above the" A" tone. For ASA threshold detection, the frequency of the B stimulus was decreased until listeners reported that they could no longer hear two separate sounds.Results: The ASA performance was significantly better for controls than the SNHL group; these differences were more obvious at higher frequencies. We found no significant differences between ASA ability as a function of tone durations in both groups.Conclusions: The present study indicated that SNHL may cause a reduction in perceptual separation of the incoming acoustic information to form accurate representations of our acoustic world.

This paper presents a new approach for estimating the signal-to-noise ratio (SNR) of mixture signal, which is based on the computational auditory scene analysis (CASA). The ideal binary mask (IBM) which is generally the computational goal of CASA based systems is used to estimate the SNR of noisy speech signal. The proposed method is evaluated using IBM and some quasi-IBM masks. The method is simple and computationally efficient. Systematic evaluations show that the proposed method results in reasonable estimation of the input SNR level in a wide range of SNR values, and probable errors in estimating IBM do not affect so much the performance of the proposed system. Furthermore, simulation results show that the proposed system outperforms previous SNR estimation methods.

Background and Aim: Foundations and mechanisms of auditory scene analysis (ASA) including segregation and integration were especially reviewed in the study. I focused on the related studies using event-related potentials (ERPs).Recent Findings: Publications on ASA using ERPs from 1971 to 2014 indicate neural mechanisms of ASA in central auditory system.Conclusion: ASA results in recognition of different sound stimuli in the competing sound environment. The neural mechanisms of this process could be studied comprehensively using ERPs with good temporal resolution.

Dear Editor, In a visit to the Forensic Medicine Department of Ankara University, Turkey, some years ago, the authors of the letter noticed that the phrase "Olay Yeri Incelemesi" means "Event Scene Investigation" rather than "Crime Scene Investigation" is used in the country [1]. When we asked for the cause, the answer was convincing: "While investigating the scene, it is frequently observed that there was only one event or incident and no crime at all. So there is no need to endanger the mental health of citizens and society by misrepresenting like Westerners". As faculty members forensic medicine specialists, we did extensive searches on the Internet and academic databases (in English, Farsi, and Turkish), but we did not find a similar case in other countries. However, the response of our Turkish neighbors influenced our thinking and inevitably led the authors to write this letter. . .

Analyzing motion patterns in traffic videos can be exploited directly to generate highlevel descriptions of the video contents. Such descriptions may further be employed in different traffic applications such as traffic phase detection and abnormal event detection. One of the most recent and successful unsupervised methods for complex traffic scene analysis is based on topic models. In this paper, a two-level Sparse Topical Coding (STC) topic model is proposed to analyze traffic surveillance video sequences which contain hierarchical patterns with complicated motions and co-occurrences. The first level STC model is applied to automatically cluster optical flow features into motion patterns. Then, the second level STC model is used to cluster motion patterns into traffic phases. Experiments on a real world traffic dataset demonstrate the effectiveness of the proposed method against conventional onelevel topic model based methods. The results show that our two-level STC can successfully discover not only the lower level activities but also the higher level traffic phases, which makes a more appropriate interpretation of traffic scenes. Furthermore, based on the two-level structure, either activity anomalies or traffic phase anomalies can be detected, which cannot be achieved by the one-level structure.

In this study, we aim to use new deep-learning tools and convolutional neural networks for traffic analysis. ResNeXt architecture, one of the most potent architectures and has attracted much attention in various fields, has been proposed to examine the scene, and classify it into three categories: cars, bikes (bicycles/motorcycles), and pedestrians. Previous studies have focused more on one type of classification and reported only human-facial recognition or vehicle detection. In contrast, the proposed method uses precise architecture to perform the classification of three classes. The proposed plan has been implemented in several steps: the first stage is to divide the critical objects. In the next step, the characteristics of the obtained objects are extracted to classify the process into three classes. Experiments have been conducted on different and essential datasets such as high-traffic, low-quality, real-time scenes. Essential evaluation criteria such as accuracy, sensitivity, and specificity show that the performance of the proposed method has improved compared to the methods being compared. The accuracy criterion reached more than 92%, sensitivity about 89%, and specially to 90.25%. The proposed method can be used to implement intelligent cities, public safety, and metropolitan decisions and use the results in urban management, predictive modeling of lost data management, sequential data management, and generalizability.

Background and Aim: The central auditory nervous system has the ability to perceptually group similar sounds and segregates different sounds called auditory stream segregation or auditory streaming or auditory scene analysis. Identification of a change in spectral profile when the amplitude of a component of complex tone is changed is referred to as Spectral profile analysis. It serves as an important cue in auditory stream segregation as the spectra of the sound source vary. The aim of the study was to assess auditory stream segregation in individuals with cochlear pathology (CP) and auditory neuropathy spectrum disorder. Methods: In the present study, three groups of participants were included. Experimental groups included 21 ears in each group with cochlear hearing loss or auditory neuropathy spectrum disorders (ANSD) and control group with 21 ears with normal hearing. Profile analysis was assessed using "mlp" toolbox, which implements a maximum likelihood procedure in MATLAB. It was assessed at four frequencies (250 Hz, 500 Hz, 750 Hz, and 1000 Hz) for all three groups. Results: The results of the study indicate that the profile analysis threshold (at all four frequencies) was significantly poorer for individuals with CP or ANSD compared to the control group. Although, cochlear pathology group performed better than ANSD group. Conclusion: This could be because of poor spectral and temporal processing due to loss of outer hair cells at the level of the basilar membrane in cochlear pathology patients and due to the demyelination of auditory neurons in individuals with ANSD.

Background and Objective:  The challenges present at various stages of crime scene investigation are among the most significant reasons for not achieving desirable results. Managers and experts in the field of crime scene investigation within the Criminal Investigation Department need to be aware of the existing challenges and the role each plays in the crime scene investigation process. Therefore, the aim of this research is to analyze the challenges facing the police in crime scene investigation and to provide appropriate solutions. Methodology:  This research is applied in terms of its objective and is developed with a qualitative (Delphi) approach. The data collection tools include document studies and in-depth interviews with the statistical population. The statistical population consists of the staff of the Tehran Criminal Investigation Department, with the Delphi panel comprising 44 crime scene investigation experts who have sufficient knowledge and experience in this field. They were selected using the snowball sampling method until theoretical saturation was achieved, and interviews were conducted with them in three stages. High agreement among respondents to the questions indicates the reliability of the research. Findings:  The challenges of crime scene investigation are categorized into two dimensions: 1. “Technical Dimension” and 2. “Managerial Dimension. ” The most important challenges include the lack of specialized and professional software, the crowding and congestion of unauthorized individuals, contamination and destruction of evidence, lack of strong coordination among different actors, absence of a cohesive and centralized management center, shortage of specialized equipment in forensic laboratories, weakness in public education, and weakness in the protection of the crime scene and evidence by first responders. Results:  Accurate initial assessment, sufficient training of investigating officers, use of advanced tools and equipment, comprehensive collection of evidence, and thorough protection of the crime scene play a significant role in reducing and addressing the challenges of crime scene investigation.