Sound Scene Analysis
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Sound scene analysis is the study that involves auditory scenes by use of a computer, in the scene, CASA system which involves a machine listening. This system aims to separate mixtures of audio sounds just the same way human being does. It bares a similar relationship to blind signal separation. This does use more than one microphone in recording of and enrollment. This in many occasions is attributed with the ability of the human being only capable to listen to a speaker’s voice with one person speaking at a time; when many speakers are involved, it's become rather hard for a human to separate the voice and get the required information. Due to this, it led to the invention of CASA.
However, sound scene analysis has to process of organizing information; these are grouped and segmentation. The segmentation part is the part that involved the separation of different sound frequencies so as to obtain specific information. It is majorly use in tracking of phone calls. The auditory scene is divided into T-F segments, each is investigated and required information is obtained. On the other hand, grouping involves the combination of the frequencies in order to obtain specific reliable information. Today, more than ever, workers are facing extremely demanding workloads. Due to the recent economic downturn that has led to budget cuts, furloughs and layoffs, people struggle to keep their jobs and often choose to take online courses to increase their level of education and make themselves more marketable or find a second job just to make ends meet.
Consequently this has increased the number of tasks and responsibilities that many people perform over a given time frame. For example, a power plant worker responsible for monitoring system status that has chosen to take online or part-time courses to increase his/her level of education may be completing course work during idle work hours. A nurse monitoring patients' health status may also be preparing a paper for a class during the workday. Other commonly combined tasks include talking on the telephone when driving a car, or using multiple information systems concurrently for decision making. However, it would be interesting to assess how effective and accurate these people are when their performance on both tasks are measured. The purpose of this paper is to mimic such an environment by is people performing a directional signal detection task while at the same time solving algebraic problems.
Due to this, there was a need for a research to enable people obtain information. ASA conducted a research and it came with the computational auditory scene analysis, as mentioned earlier, it is the use of computers to study audible sound. It aimed to organize different sets of sounds with a basic principle behind it. In this way one could easily tell the time and distance between targets based on the frequencies and the wavelength of the sound wave. In many occasions, it seeks to organize simultaneous sounds. This could only be done by use of the sequential organizing by the computer software. This implies, that sound produced from a single speaker could either be separated (segmentation) into T-F segments i.e. a single stream or could be joined (grouping) and done could easily determine what was communicated. In this way, one could easily pick a specific sound by a given person. In most cases, a human being cannot do this. Nevertheless, this method could any be possible if one can know the character of the Sequoia speaker.
This study involves the exploration of bottom up methods for sequential grouping. However, the ability to obtain a good sequential organization framework of the message (sound) is based on the ability of the speaker performance; this implies that a good speaker will ease the computational objective. On the other hand, it has been proposed that huge speakers are capable of delivering good and qualitative stream that can ease the sequential grouping. Therefore, co-channel speaker recognition at times becomes easy to do the grouping. That is co-channel speech takes place when a two outputs (stream) in transmitted in a single channel. This therefore, at time will require one to search for an optional grouping upon which the speech can be segmented. This is always done with the intensions to reduce the search space and time for communication. To achieve this, some hypothesis pruning strategies may be set and proposed to be used in the system, the system will not only involve grouping performance, but also speech recognition accuracy. This implies that it will not only see the end result but also the efficiency to determine the exact speech in the investigation. The more accurate the speech recognition, the more efficient the hypothesis was. Thus, systematic evaluation of the study plays a crucial part in the sound scene analysis.
The proposed model based grouping system is then modified to accommodate many speakers (talkers) also speechless intrusions using advanced genetic models. This can be able to be obtained and manipulated whether there is interference or not. Interference in this case means all possible blockages that may alter the computer from obtain a clear speech. Afterwards, the system a further be extended to accommodate more people (speaker) and at a noisy environment. This implies that system is made to cope up with multiple speakers and with more interference. As earlier mentioned, the system should be able to recognize the sound with or without interference. The interference can be coming from different sources but with system being able to cope up with this, one can sequentially tell the origin of the speech (sound) irrespective of the distance. In other words, the system is made to accommodate speech from far; this is obtained by the use of genetic models, it does employ the quantization method which involves the extraction of larger speakers and performing of grouping. The resultant outcomes will be moderate i.e. lower than that of known speaker model; this is the only dis advantage about it.
Even more interesting than the factors that lead to segregation of sounds into separate auditory streams are the effects that this has on our perceptual experience of the sound. The first of these effects is that the details of temporal order are available to our perception only when they concern sounds in the same stream. For example, when the galloping sequence, HLH-HLH-..., segregates very strongly into two streams, it is difficult to judge whether the L tone occurs exactly halfway in time between the two H tones. A second factor that emerges from the organization into streams is rhythm (which tends to be formed by sounds in the same auditory stream). In the galloping pattern, described earlier, the triplet rhythm of the H_L_H gallop was audible only so long as H and L were perceived to be in a single stream. A third result of the formation of streams is melody. Melodies tend to emerge from tones perceived as being in the same auditory stream. For example, when the galloping sequence split into two streams, the up and down (simple) melody of the gallop was lost and replaced by two streams, each of which contained only a simple pitch. This means that when a composer wants the listener to hear more than one melody (each in a different pitch range), the pitch ranges must be well separated. If they draw close together, a note may be "captured" perceptually into the wrong melody. You can listen to an illustration of how a melody depends on all its notes being in the same stream, isolated from other sounds that might also be present. Suppose we take a simple melody, "Mary Had a Little Lamb," and insert a note of a randomly chosen pitch between every two notes of the melody. If the distractor notes are chosen from the same pitch range as the melody's notes, the melody is impossible to hear. However, if the distractor notes fall outside the range of the melody (say an octave above it) and form a separate stream, the melody can be heard clearly in its own stream.
In addition to the systematic and sequential grouping, this can be methods can be applied in making of the robust; the robust will be able to detect and recognize different sounds. Tis will make it possible for a robust to know what has been told by use of simple or complex speech. It can also be able to tell the person spoke since it will be able to recognize the sound source. The detection will only be easy if the speech extraction method that was proposed earlier it significance to improve and increase the recognition performance. In some special cases, the missing- data in a speech recognition, can be combined with the use of the CASA by use of a processor to be able to obtain a full recognized speech. This can be done regardless of the environment whether it is noisy or not. Later, a given general solution can be established to the robust speaker recognition; this will be aiming to improve the performance of the recognition. This is always done with the imitation of a human being; how human is able to detect and respond to sounds.
In addition, the principles of algorithm, and it application provides a coherent and comprehensive account to the CASA in terms of setting underlying principles that provide framework in organization of sound. This account for the improvement of the hearing of the robust for example, these studies have been applicable to the robust where they have been made to recognized and detect sound effectively. This field has made it possible for human being to determine and carryout experiment in areas that he or she cannot access. For example, flying in to the space, instead a person going to the space, it is the robust which have been made to go. This is a potential application of the technology that has yield positive outcomes. This can be applied in many field of study.
These has been able to solve the demand of the employers in the industries since at time there require extensive labors which could work under strict and intensive conditions. Robust could easily adapt to such conditions that the employers need. At times, there are some extreme weather conditions which may not allow human to work; in such occasions the robust are replaced with human beings. This is has been made possible since robust could easily obey command of speech just like man. They are also able to work accurately and efficiently. Therefore, one can easily conclude that with the sound system analysis, the effectiveness and accuracy of the robust have been improved.
Another field of application is the neural CASA. When making the robust, it has been determined that it can be in a way that they can be to deter stimulus. This implies that they have been made in a way that they can sense stimulus just like the way human being can sense the. This make it possible for the robust to detect some conditions that man (human being) cannot sustain. For instance, a robust may be sent to detect some environmental condition in a different planet. They can tell the weather condition that can survive. This is the reason that when conduction researches, robust are the equipment that are used before human try them.
In addition to these, it can be applied in the musical audio signal. The influence of sound in shaping performance has been central to the filmic medium-and to the comparison of film and theater-since the introduction of sync sound in the late 1920s. Critics writing at the time, such as Rudolf Arnheim, proclaimed that "sound film... a means of 'canning' theater," a "replacement for theater," and that "sound film is theater which has been technically perfected" Those who focused on the work of the actor, specifically, also remained tied to a comparison with theater. René Clair, however, also identified differences between the two, ascribing a more nuanced and realistic style of acting to sound film. Writing in 1929, Clair praised cinematic actors for the "total lack of theatrical affectation in their voices" and claimed that the "actors show remarkable flexibility... their acting with speech is as natural as was their silent acting in earlier films". These quotes clearly illustrate two important points: the perception of film as "the same" as theatrical performance because of the synchronicity in the actor's visual and aural representation, and the perception of film acting as different from theatrical performance because of the availability of a more realist mode of speaking. In addition, it had been known that human mind cannot carry out sound separation in a physical mixture. With the use of sound scene analysis sound can be broken down into separate stream such as chords, melodies and bass. These make it possible to integrate and come up with beats. For example, the western music demands for some beats and mixture of tones and rhyme. These cannot be easily achieved without a proper knowledge of sound scene analysis. For these reason, people have been able to come up with different sound in a choir and songs. In addition, it quite difficult for some people or human to detect some specific frequencies of sound, therefore, in music industry, there are developed keys upon which one can easily use to accomplish the intended outcome sound. This has been able to the study of bottom top integration. Thus, with integration of sound detection analysis using top down integration, one can be able to track beats in music.
Consequently, the use one can also say that binaural in another application of the CASA. This is where models such as that of batch processing has been used to improve the quality of a microphone. This is also use in speech separation components. For example, this method can be used in tracking of voice calls whether it was done by a radio call or a cell phone. In many situations this methods are used by the police to investigate some cases. Speech signals can be separated and in a single stream of sound frequencies. Although sound and image, as they relate to an actor's performance, are separated in the cinematic recording process, mainstream Hollywood film tends to hide this separation to preserve the illusion of "reality." That is to say, careful attention is paid to synchronizing the sound track and the visual track, so that the correspondence of the actor's spoken words with his or her moving lips will create the semblance of a live, natural performance rather than an artificial, recorded one. This fusion of sound and image tends to generate more full-blown analyses of actors' performances-analyses that tie the sound or vocal aspect of the performance to the visual or bodily one.
Nevertheless, one can interpret the effects of speed as bringing each H tone closer to the next H tone, and each L tone closer to the next L tone. Compare Panel c (slow) with Panel d (fast), with the same frequency separation. Think of each panel as a two-dimensional surface on which the tones are laid out. Both time and frequency contribute to the "distance" between pairs of tones. Tones that are closer to one another on this surface tend to group together. At low speeds (Panel c), each H tone is closer to the following L tone than it is to the following H tone; so it groups with the L tone. As the sequence speeds up (Panel d), each H tone comes closer in time to the next H tone and groups with it, so that the net frequency-by-time distance favors its grouping with the next H in preference to the next L. The eye, looking at Panel d, sees the same two groupings of the horizontal bars that represent the tones. We could just as easily have brought the sounds closer together in time by keeping their tempo constant but increasing the length (duration) of each tone.
This is an example of sequential grouping, since no two sounds are present at the same time. It shows that there is a tendency for similar sounds to group together to form streams and that both nearness in frequency and in time are grounds for treating sounds as similar. The Gestalt psychologists had shown that, in vision, objects that are nearer in space (or more similar) tend to form tighter perceptual clusters. The same principle seems to apply to audition.
The preceding example used the pitch of pure tones (based on their frequencies) as the variable that defined similarity, but there are many other ways in which short simple sounds can be similar or dissimilar. Among them are: 1) timbre (differences in the sound quality of tones despite identical pitches and loudness’s) - note that the difference between the vowel sounds "ee" and "ah" can be thought of as a timbre difference; 2) spectral similarity (i.e., to what extent they share frequency components [e.g., for noise bursts that have no pitch]); 3) temporal properties, such as the abruptness of onset of sounds; 4) location in space; and, 5) intensity.
However, when the galloping sequence breaks apart into two perceived sequences, a high one and a low one, we say that a single auditory stream has split apart into two streams. In vision, we refer to the result of grouping as an object (when the result is perceived as a unit), or as a perceived group of objects (when the result is a cluster of separate objects). In hearing, we refer to the result of auditory grouping as an auditory object or a perceived sound (when it creates a single sound), and as an auditory stream (when it creates a sequence that unfolds over time). The perception of a stream is the brain's way of concluding (correctly or incorrectly) that sounds included in the stream have been emitted over time by the same sound source (e.g., a drum, a voice, or an automobile).
Therefore, When Auditory Scene Analysis (ASA) sorts out components of the incoming mixture and allocates them to different perceived sounds, these influences many aspects of what we hear, because only the frequency components assigned to the same sound by ASA will affect the experienced qualities of that sound. Examples are the pitch and timbre of the sound, both of which are based on the set of harmonics assigned to that sound.
Even the loudness of sounds can be affected by their perceptual organization. When two soft sounds occur at the same time, their energies are added up at the ear of the listener, giving the same energy as a single loud signal. So when our ear receives that loud signal, the auditory system has to form an interpretation of what we are listening to (i.e., is it two or more soft sources of sound or one loud one?). The perceptual process makes that decision using the cues for separating concurrent sounds, and this gives rise to the loudness experience (s).
In conclusion, the personal experience of the researcher has not fared well in scientific psychology. Since the failure of Kitchener’s Introspections in the early 20th century, and the rise of Behaviorism, scientific psychology has harbored a deep suspicion of the experience of the researcher as an acceptable tool in research. One would think that the study of perception would be exempt from this suspicion, since the subject matter of the psychology of perception is supposed to be about how a person's experience is derived from sensory input. Instead, academic psychology, in its behavioristic zeal, redefined perception as the ability to respond differently to different stimuli - brings it into the stimulus-response framework. Despite Behaviorism’s fall from grace, psychology still insists on a behavioristic research methodology.
The example of sequential grouping, since no two sounds are present at the same time. It shows that there is a tendency for similar sounds to group together to form streams and that both nearness in frequency and in time are grounds for treating sounds as similar. The Gestalt psychologists had shown that, in vision, objects that are nearer in space (or more similar) tend to form tighter perceptual clusters. The same principle seems to apply to audition.
Last but not least, grouping of sounds that occur in a sequence, but the auditory system must also deal with environmental sounds that overlap in time. When the signals travelling from ear to brain represent a mixture of sounds that are present at the same time, the auditory system must sort out this information into a set of concurrent streams. If we reexamine the spectrogram of a mixture as discussed earlier in this paper, we see that a vertical slice contains more than one frequency (a single frequency would be shown as a single thin horizontal line). Yet it is not immediately obvious how the frequencies in this slice should be allocated as components of various concurrent sounds.
In conclusion, sound scene analysis is the study of sound using computer systems. It bares the same principles as how human beings perceive sound. With this technology, human being are able to combine and separate sound of different frequencies regardless of interference. This has led to improvement in musical industry, ASA and also ASR in the robots hearing ability.
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