A sound classification system for the automatic recognition of the acoustic environment in a hearing aid is discussed. The system distinguishes the four sound classes “clean speech,” “speech in noise,” “noise,” and “music.” A number of features that are inspired by auditory scene analysis are extracted from the sound signal. These features describe amplitude modulations, spectral profile, harmonicity, amplitude onsets, and rhythm. They are evaluated together with diﬀerent pattern classifiers. Simple classifiers, such as rule-based and minimum-distance classifiers, are compared with more complex approaches, such as Bayes classifier, neural network, and hidden Markov model. Sounds from a large database are employed for both training and testing of the system. The achieved recognition rates are very high except for the class “speech in noise.” Problems arise in the classification of compressed pop music, strongly reverberated speech, and tonal or fluctuating noises.
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Another emerging area of research within 3D scene anal- ysis relates to Volumetric Reasoning. Here the applica- tion of logic based algorithms to existing object clusters is used to improve segmentation and clustering accuracy.  utilises the notion that clusters in a scene should be in a state of rest when simulation techniques are applied. Thus by using an iterative process clusters are grouped until such time as the scene is at equilibrium.  proposes a method that better fits bounding shapes to RGB-D clusters based on the premise that a good 3D representation of a scene is sta- ble, fits the data well and is self-supporting. Battaglia  introduces the idea of a ‘intuitive physics engine (IPE)’ that tries to mimic a humans cognitive simulation process when analysing a scene. It is also worth mentioning the follow- ing papers that consider similar concepts and approaches for scene analysis [22, 11, 18, 20, 21, 9, 19, 8].
A lot of eﬀort has been made in Computational Auditory Scene Analysis (CASA) to segregate target speech from monaural mixtures. Based on the principle of CASA, this article proposes an improved algorithm for monaural speech segregation. To extract the energy feature more accurately, the proposed algorithm improves the threshold selection for response energy in initial segmentation stage. Since the resulting mask map often contains broken auditory element groups after grouping stage, a smoothing stage is proposed based on morphological image processing. Through the combination of erosion and dilation operations, we suppress the intrusions by removing the unwanted particles and enhance the segregated speech by complementing the broken auditory elements. Systematic
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this relation, the order of the reference objects cannot be inverted otherwise the type of the relation is modified. Indeed, if the target object is above only one of the reference objects, other ternary spatial relations can be then applied. Hence, this ternary modeling leads to a more discriminating relation than the traditional ones in particular in the case of crowded scene analysis.
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Since BSS is only based on multiple sensors records and our interest is on the monaural speech segregation, we will be focusing later only on these approaches. Several meth- ods have been proposed for monaural speech separation, like spectral subtraction , subspace analysis , hidden Markov modeling , and sinusoidal modeling . These approaches usually suppose certain properties of interfer- ence and then separate composite speech based on these hypotheses. That is why their capacity for speech segre- gation is much limited than the human capacity. Thus, we are interested by the study of the computational auditory scene analysis (CASA).
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Other popular methods that can easily integrate with video surveillance systems are commonly vision- based. Huang et al .  proposed a stereo-based head detection method for human detection in a crowded scene. They applied 3D scale-filtering to extract the likelihood evidence of heads from the stereo image. They managed to obtain good results of detecting human heads in crowds have been obtained from the experiments on real scene. Other researchers such Zhao et al .  proposed people counting method based on face tracking combining a new scale invariant Kalman filter with kernel based tracking algorithm. Their proposed method demonstrated a good
Due to the common sense aspects associated with CSR beliefs (one thing leads to another), little effort has been directed at refining the theory of CSR within the criminal investigation arena. But there was on-going theory development in another arena, and that effort is directly applicable to the discipline of crime scene reconstruction. The area of interest is systems safety investigations also known as failure analysis. Remember that the crime scene analyst is attempting to explain a unique and rare phenomenon (the specific incident under investigation). Every incident being investigated is itself unique. Granted they involve many predictable scientific cause and effect relationships (e.g., the manner in which blood behaves under a given force, the behavior of a bullet in flight, the transfer of genetic information) which appear in many incidents, but the incident in question occurs once and only once.
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from its original value by more than 4%, listeners heard it as a separate tone. This condition could be shown by Object-related negativity (ORN) . This response overlaps in time with the n1 and p2 deflections and has a latency of 150 ms (Fig. 5). It is observed in school-aged children . The ORN can be recorded for stimuli that are unattended, such as when participants ignore the stimuli and read a book of their choice or watch a subtitled movie. Recording this response in an unattended condition likely indicates that concurrent sound segregation occurs independent of a listener’s attention. The ORN amplitude is usually the largest at central and frontocentral sites and inverts polarity at the mastoid sites. It is indicated that its generators are located in the supratemporal plane within the sylvian fissure . The ORN shows some similarities in latency and amplitude with MMN. Like the ORN, the MMN also has a frontocentral distri- bution and its latency peaks at about 150 ms after the onset of deviation. Both ORN and MMN to acoustic stimuli could be assumed to index bottom-up processing of ASA [45,46]. Despite the similarities, there are differences between these responses. One of the most important differences is that while MMN is highly sensitive to the perceptual context, the ORN is not. In addition, the MMN is elicited only by rare deviant stimuli whereas the ORN is elicited by mistuned stimuli regardless of whether they are presented occasionally or frequently . Hence, the MMN reflects a mismatch between the incoming auditory stimulus and what is expected based on recently occurring stimuli, whereas the ORN reflects a discrepancy between the mistuned harmonic and what is expected on the basis of the current stimulus. It is indicated that the MMN is an index of sequential integration because its elicitation depends on the extraction of regul- arities over several seconds. In contrast, the ORN is assumed to index concurrent sound segregation and depends on a simultaneous spe- ctral analysis of the incoming acoustic waveform . Also, the ORN and MMN can be differentiated based on their scalp Fig. 5. A. Mistuned stimulus paradigm used to
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Abstract: In 1992 a double murder in El Paso County, Colorado took the lives of Kermode and Pamela Jordan. Physical evidence strongly implicated one juvenile, but failed to establish guilt for his juvenile accomplice. The statements by both boys were ruled inadmissible, which led the District Attorney to pursue a crime scene analysis and reconstruction expert to explain to the jury that one attacker could not accomplish all of the events that took place during the crime.
Abstract: Auteur theory which arose in France in the late 1940s considers the director of a film as a gifted creative individual who leaves his imprint, if not all, in most of his films. With his distinguishing film-making style and personal leitmotifs, the creator’s signature sweeps through his films and the director triumphs in articulating his personality through them. In Malayalam cinema, where auteurs are finite, the director Shyamaprasad has managed to be distinguished as one, with his recurring style and themes. A mise-en-scene analysis and content analysis of his films reveals that he can be appraised as a successful auteur based on the consistency with which the director expresses a uniquely personal point of view in his films through the use of recurring themes, characters and situations. His films transcend the individual and become universal with the distinctive imprint of the unconventional director.
The text provides specific details on crime scene analysis and reconstruc- tion in explaining the scientific methodology involved in the process. The authors provide an excellent historical perspective to acquaint the reader with the significant chronology of the application of this technique. The authors also provide excellent information on distinguishing crime scene analysis from behavioral analysis and discuss the considerations involved in the recon- struction of the crime. There is an entire chapter dedicated to the important subject of proper terminology to be utilized in these analyses. Bloodstain Pattern Analysis explains the complex mechanics of blood spatter analysis with chapters that address the dynamics of reconstruction, such as determin- ing motion and directionality, convergence and the point of origin, evaluating impact spatter bloodstains, the characteristic patterns of blood which aid in analysis and the proper documentation of blood stains and the reconstruc- tion of a crime. The authors have included two chapters on software appli- cations and demonstrative evidence for court presentation. Most significant in the Second Edition is the addition of over 100 photographs, which graph- ically illustrate the dynamics of bloodstain patterns, practical applications, case histories and excellent appendices.
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Qualitative Analysis. Some qualitative examples are il- lustrated in Fig. 4. In the first sample, h-attn-rank expresses the same meaning within the generated sentences, which is “the bride and groom are happy”. For our HPSR model, the sentence indicates that it is a ’wedding’ (in red) in the be- ginning, and then mentions the cake-cutting (in blue). In the end, the story expresses praise to the bride and groom, which is more similar to a story by human. Compared with the story generated by h-attn-rank, HPSR generates more co- herent sentences and yields a complete story. In the second sample, the story generated by HPSR tells us the property of museum ’airplane museum’(in red). And HPSR generates different words for describing the aircrafts, such as ’plane’, ’aircraft’ and ’airplane’ (in blue), although it fails to under- stand the relationship of them. In contrast, h-attn-rank only tell us just ’planes’ in ’museum’.
The first component is Scene Control that functions to create or draw objects in the scene and also manage the scene contents by renders the specified views in real time. Next, Scenario Control is control component in generate and distribute viewing parameters as well as send rendering commands to the related component. GUI is component provides human-computer interface for user to interact with the system. Vehicle Control is component includes motion control and car cab I/O control.Lastly, Audio Control is component able to produce sound effects like engine, tire, wind, and tire squeal. All this compenent will interact between themselves to manipulate the database content visual model of the simulator in Visual Database.
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In some applications, scene mining is based on combination of low-level features and semantic features which are extracted from other sources such as text and audio.  has used video and text in sport scene mining that its feature vector consists of textual and low-level features. In the game action, a complete description of playing game is instantly produced in sentences and archived for later use. Each sentences can be represented as: <sentence time, sentence content>. For each replay scene, those sentences within the window <T replay - T l ,
It is worth noting that through using the distance output, the devised method can be readily extended into 3D “abnormal regions” in a crowd scene, henceforth laying a foundation for denoting semantically defined crowd events, such as “gathering”, “dispersing”, and other squared behaviours. Future work will see the recognition system being developed to adopt visual words from “abnormal” STT slices through training methods such as Latent Dirichlet allocation for classifying different events.
Identifying objects within a scene is a key goal in the field of computer vision. One method of describing objects within a scene is to identify their structure through analysis of their motion between multiple images, a process commonly referred to as Structure from Motion (SfM). This technique of analyzing objects has its roots in traditional photogram- metry, though the standard goal of SfM techniques lies in object identification rather than mensuration. To that end, the SfM algorithm chain assumes little or no information about the imaging platform. The computer vision community has developed a number of com- plex processes to estimate camera pose, i.e., the sensor position and orientation, but these methods are limited to estimating parameters in a relative sense [25, 32]. Consequently, any estimated object structure is in the same relativistic coordinate system. Precise geo- graphic measurements of objects, cannot be directly extracted in this coordinate system without additional information.
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If processing of the scene does not re- veal any indications of the position of the shooter, consider the possibility that the shots could have been fired from a more distant position. The search perimeter should be extended in the direction deter- mined by the trajectory analysis in an effort to locate the shooting position and secure evidence as quickly as possible. As search- ers walk along the trajectory, they should fan out to encompass at least a five degree margin for error on either side of the trajec- tory. The searchers should be looking for evidence of a shooting position that could include cartridge cases, footwear impres- sions, or other evidence left behind by the shooter. They should also look for any dis- turbed area such as flattened grass, cleared ground cover, or objects moved in order to provide a line of sight to the victim’s loca- tion. Once the shooting position is identi- fied the searchers should secure that area
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These files were then used to test the algorithm. The files that contain only a single-note scale test the time tracking portion, while the files containing patterns of thirds and the musical example tested both the time tracking and the multipitch analysis. The first musical example is fairly complex in that it contains notes that are held for a longer duration than others and it certainly does not follow a structured pattern like the scale of thirds does. The second musical example tests the algorithm’s ability to detect more than two simultaneous notes. To test the temporal resolution, the file containing 16th notes at 150 bpm was created. This file tests the algorithm’s ability to recognize and detect notes that occur in an extremely short time period. This is also meant to test the proposed modification to expand the Hamming window size to 100 ms.
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The fire happened in a two-storied residence located at Zhong Zheng Road, Sikou Town, ChiayiCounty at around one o’clock June 11 th , 2010. There was an office, living room, kitchen, and storage on the first floor, and the bedrooms on the second floor. The fire was started due to man-made fire; the arsonist broke the glass of the storage on the first floor, poured gasoline in the storage, and then ignited with papers. Fire and heavy smoke soon spread to the whole building. Because the building was pretty small and it was mostly partitioned with wooden boards along with a lot of combustibles in it, the entire building was seriously damaged except for the office on the right side of the first floor. This fire caused the tragedy of three deaths and one injury. The photo of the exterior of the fire scene and Configuration of 3D Model is presented in Figure 1.
Hesmondhalgh (2005), suggests that the concept of scene has generally been used in two ways: either to understand ‘place bound’ music cultures (such as in the UK the ‘Mersey Beat’ of the 1960s or the ‘Madchester’ scene of the late 1980s/early 1990s) (as used by Shank 1994) or as ‘complex spatial flows of music affiliations’ (such as could be characterized in the ‘goth’ scene) (as employed by Straw 1991). Though Hesmondhalgh highlights these two separate usages as incompatible, and therefore questions the validity of the concept of scene, Longhurst suggests that these are not necessarily conflictual readings, and in fact, highlights the very usefulness of this term. For two main reasons: first, scene allows for an understanding of how elective belongings are lived out and experienced in our ordinary lives, and second, how scenes take on ‘extraordinary’ meaning at certain times and in specific locations. Hence, this concept places particular emphasis on the importance of ‘place’.
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