Audio or music fingerprints can be utilize to implement an economical music identification system on a
million-song library, however the system needs great deal of memory to carry the fingerprints and indexes.
Therefore, for a large-scale audio library, memory
imposes a restriction on the speed of music
identifications. So, we propose an efficient music
identification system which used a kind of space-saving
audio fingerprints. For saving space, original finger representations are sub-sample and only one quarters
of the original data is reserved. In this approach,
memory demand is far reduced and therefore the search
speed is criticalincreasing whereas the lustiness and dependability are well preserved. Mapping
audio information to time and frequency domain for
the classification, retrieval or identification tasks
presents four principal challenges. The dimension o
f the input should be considerably reduced;
the ensuing options should be strong to possible distortions of the input; the feature should be informative
for the task at hand simple. We propose distortion
free system which fulfils all four of these requirements.
Extensive study has been done to compare our system
with the already existing ones, and the results sh
ow
that our system requires less memory, provides fast
results and achieves comparable accuracy for a large-
scale database.
Streaming Audio Using MPEG–7 Audio Spectrum Envelope to Enable Self-similarit...TELKOMNIKA JOURNAL
The ability of traditional packet level Forward Error Correction approaches can limit errors for
small sporadic network losses but when dropouts of large portions occur listening quality becomes an
issue. Services such as audio-on-demand drastically increase the loads on networks therefore new, robust
and highly efficient coding algorithms are necessary. One method overlooked to date, which can work
alongside existing audio compression schemes, is that which takes account of the semantics and natural
repetition of music through meta-data tagging. Similarity detection within polyphonic audio has presented
problematic challenges within the field of Music Information Retrieval. We present a system which works
at the content level thus rendering it applicable in existing streaming services. Using the MPEG–7 Audio
Spectrum Envelope (ASE) gives features for extraction and combined with k-means clustering enables
self-similarity to be performed within polyphonic audio.
Deep Learning with Audio Signals: Prepare, Process, Design, ExpectKeunwoo Choi
Is deep learning Alchemy? No! But it heavily relies on tips and tricks, a set of common wisdom that probably works for similar problems. In this talk, I’ll introduce what the audio/music research societies have discovered while playing with deep learning when it comes to audio classification and regression -- how to prepare the audio data and preprocess them, how to design the networks (or choose which one to steal from), and what we can expect as a result.
This presentation shares some basic concepts about audio processing and image processing using MATLAB and was used as teaching material for an introductory workshop.
The following resources come from the 2009/10 B.Sc in Media Technology and Digital Broadcast (course number 2ELE0073) from the University of Hertfordshire. All the mini projects are designed as level two modules of the undergraduate programmes.
AN EFFICIENT PEAK VALLEY DETECTION BASED VAD ALGORITHM FOR ROBUST DETECTION O...cscpconf
Voice Activity Detection (VAD) problem considers detecting the presence of speech in a noisy signal. The speech/non-speech classification task is not as trivial as it appears, and most of the VAD algorithms fail when the level of background noise increases. In this research we are presenting a new technique for Voice Activity Detection (VAD) in EEG collected brain stem speech evoked potentials data [7, 8, 9]. This one is spectral subtraction method in which we have developed our own mathematical formula for the peak valley detection (PVD) of the frequency spectra to detect the voice activity [1]. The purpose of this research is to compare the performance of this SNR based PVD (SNRPVD) method over Zero-Crossing rate detector [5] and statistical analysis based algorithms [10]. We have put into application of these three algorithms on these particular data sets of this experiment [7, 8, 9] and VAD is verified and compared the results of these three. MATLAB routines were developed on these particular methodologies. Finally we concluded that the method of SNRPVD surely performing better than the ZCR and statistical algorithms.
An efficient peak valley detection based vad algorithm for robust detection o...csandit
Biometrics is science of measuring and statistically analyzing biological data. Biometric system
establishes identity of a person based on unique physical or behavioral characteristic possessed
by an individual. Behavioral biometrics measures characteristics which are acquired naturally
over time. Physical biometrics measures inherent physical characteristics on an individual.
Over the last few decades enormous attention is drawn towards ocular biometrics. Cues
provided by ocular region have led to exploration of newer traits. Feasibility of periocular
region as a useful biometric trait has been explored recently. With the promising results of
preliminary examination, research towards periocular region is currently gaining lot of
prominence. Researchers have analyzed various techniques of feature extraction and
classification in the periocular region. This paper investigates the effect of using Lower Central
Periocular Region (LCPR) for identification. The results obtained are comparable with those
acquired for full periocular texture features with an advantage of reduced periocular area.
Streaming Audio Using MPEG–7 Audio Spectrum Envelope to Enable Self-similarit...TELKOMNIKA JOURNAL
The ability of traditional packet level Forward Error Correction approaches can limit errors for
small sporadic network losses but when dropouts of large portions occur listening quality becomes an
issue. Services such as audio-on-demand drastically increase the loads on networks therefore new, robust
and highly efficient coding algorithms are necessary. One method overlooked to date, which can work
alongside existing audio compression schemes, is that which takes account of the semantics and natural
repetition of music through meta-data tagging. Similarity detection within polyphonic audio has presented
problematic challenges within the field of Music Information Retrieval. We present a system which works
at the content level thus rendering it applicable in existing streaming services. Using the MPEG–7 Audio
Spectrum Envelope (ASE) gives features for extraction and combined with k-means clustering enables
self-similarity to be performed within polyphonic audio.
Deep Learning with Audio Signals: Prepare, Process, Design, ExpectKeunwoo Choi
Is deep learning Alchemy? No! But it heavily relies on tips and tricks, a set of common wisdom that probably works for similar problems. In this talk, I’ll introduce what the audio/music research societies have discovered while playing with deep learning when it comes to audio classification and regression -- how to prepare the audio data and preprocess them, how to design the networks (or choose which one to steal from), and what we can expect as a result.
This presentation shares some basic concepts about audio processing and image processing using MATLAB and was used as teaching material for an introductory workshop.
The following resources come from the 2009/10 B.Sc in Media Technology and Digital Broadcast (course number 2ELE0073) from the University of Hertfordshire. All the mini projects are designed as level two modules of the undergraduate programmes.
AN EFFICIENT PEAK VALLEY DETECTION BASED VAD ALGORITHM FOR ROBUST DETECTION O...cscpconf
Voice Activity Detection (VAD) problem considers detecting the presence of speech in a noisy signal. The speech/non-speech classification task is not as trivial as it appears, and most of the VAD algorithms fail when the level of background noise increases. In this research we are presenting a new technique for Voice Activity Detection (VAD) in EEG collected brain stem speech evoked potentials data [7, 8, 9]. This one is spectral subtraction method in which we have developed our own mathematical formula for the peak valley detection (PVD) of the frequency spectra to detect the voice activity [1]. The purpose of this research is to compare the performance of this SNR based PVD (SNRPVD) method over Zero-Crossing rate detector [5] and statistical analysis based algorithms [10]. We have put into application of these three algorithms on these particular data sets of this experiment [7, 8, 9] and VAD is verified and compared the results of these three. MATLAB routines were developed on these particular methodologies. Finally we concluded that the method of SNRPVD surely performing better than the ZCR and statistical algorithms.
An efficient peak valley detection based vad algorithm for robust detection o...csandit
Biometrics is science of measuring and statistically analyzing biological data. Biometric system
establishes identity of a person based on unique physical or behavioral characteristic possessed
by an individual. Behavioral biometrics measures characteristics which are acquired naturally
over time. Physical biometrics measures inherent physical characteristics on an individual.
Over the last few decades enormous attention is drawn towards ocular biometrics. Cues
provided by ocular region have led to exploration of newer traits. Feasibility of periocular
region as a useful biometric trait has been explored recently. With the promising results of
preliminary examination, research towards periocular region is currently gaining lot of
prominence. Researchers have analyzed various techniques of feature extraction and
classification in the periocular region. This paper investigates the effect of using Lower Central
Periocular Region (LCPR) for identification. The results obtained are comparable with those
acquired for full periocular texture features with an advantage of reduced periocular area.
A new algorithm for data compression technique using vlsiTejeswar Tej
HOW COMPRESSION IS POSSIBLE?????????
NOW A DAYS LOT OF ALGORITHMS ARE READY TO COMPRESS DATA BUT POWER IS THE MAJOR CRITERIA OF ALL.BUT MY PROJECT IS TO OVERCOME IT I..E THE NEW ALGORITHM BY
K-RLE
Types of Data compression, Lossy Compression, Lossless compression and many more. How data is compressed etc. A little extensive than CIE O level Syllabus
Erca energy efficient routing and reclusteringaciijournal
The pervasive application of wireless sensor networks (WNSs) is challenged by the scarce energy constraints of sensor nodes. En-route filtering schemes, especially commutative cipher based en-route filtering (CCEF) can saves energy with better filtering capacity. However, this approach suffer from fixed paths and inefficient underlying routing designed for ad-hoc networks. Moreover, with decrease in remaining sensor nodes, the probability of network partition increases. In this paper, we propose energy-efficient routing and re-clustering algorithm (ERCA) to address these limitations. In proposed scheme with reduction in the number of sensor nodes to certain thresh-hold the cluster size and transmission range dynamically maintain cluster node-density. Performance results show that our approach demonstrate filtering-power, better energy-efficiency, and an average gain over 285% in network lifetime.
An Intelligent Method for Accelerating the Convergence of Different Versions ...aciijournal
In a wide range of applications, solving the linear
system of equations Ax = b is appeared. One of the
best
methods to solve the large sparse asymmetric linear
systems is the simplified generalized minimal resi
dual
(SGMRES(m)) method. Also, some improved versions of
SGMRES(m) exist: SGMRES-E(m, k) and
SGMRES-DR(m, k). In this paper, an intelligent heur
istic method for accelerating the convergence of th
ree
methods SGMRES(m), SGMRES-E(m, k), and SGMRES-DR(m,
k) is proposed. The numerical results
obtained from implementation of the proposed approa
ch on several University of Florida standard
matrixes confirm the efficiency of the proposed met
hod.
SURVEY SURFER: A WEB BASED DATA GATHERING AND ANALYSIS APPLICATIONaciijournal
The most important need for a web based survey technology is speedy performance and accurate results
.Though there are variety of ways a survey can be taken manually and have it assessed manually but here
we are introducing the survey site concept where the assessment of the survey responses are created
automatically by applying certain statistics. In this paper we propose the idea of automated approach to
the analysis of the survey where the results would be evident graphically to take a wise decision. Additional
to graphical view we aim on forming a platform to view complex, tedious data in simple, understandable,
interactive and visualized form.
Web spam classification using supervised artificial neural network algorithmsaciijournal
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are more efficient, generic and highly adaptive. Neural Network based technologies have high ability of adaption as well as generalization. As per our knowledge, very little work has been done in this field using neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised learning algorithms of artificial neural network by creating classifiers for the complex problem of latest web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation learning, and Levenberg-Marquardt algorithm.
Learning strategy with groups on page based students' profilesaciijournal
Most of students desire to know about their knowledge level to perfect their exams. In learning environment the fields of study overwhelm on page with collaboration or cooperation. Students can do their exercises either individually or collaboratively with their peers. The system provides the guidelines for students' learning system about interest fields as Java in this system. Especially the system feedbacks information about exam to know their grades without teachers. The participants who answered the exam can discuss with each others because of sharing e mail and list of them.
TRUST METRICS IN RECOMMENDER SYSTEMS: A SURVEYaciijournal
Information overload is a new challenge in e-commerce sites. The problem refers to the fast growing of
information that lead following the information flow in real world be impossible. Recommender systems, as
the most successful application of information filtering, help users to find items of their interest from huge
datasets. Collaborative filtering, as the most successful technique for recommendation, utilises social
behaviours of users to detect their interests. Traditional challenges of Collaborative filtering, such as cold
start, sparcity problem, accuracy and malicious attacks, derived researchers to use new metadata to
improve accuracy of recommenders and solve the traditional problems. Trust based recommender systems
focus on trustworthy value on relation among users to make more reliable and accurate recommends. In
this paper our focus is on trust based approach and discuss about the process of making recommendation
in these method. Furthermore, we review different proposed trust metrics, as the most important step in this
process.
LOCALIZATION OF OVERLAID TEXT BASED ON NOISE INCONSISTENCIESaciijournal
In this paper, we present a novel technique for localization of caption text in video frames based on noise inconsistencies. Text is artificially added to the video after it has been captured and as such does not form part of the original video graphics. Typically, the amount of noise level is uniform across the entire captured video frame, thus, artificially embedding or overlaying text on the video introduces yet another segment of noise level. Therefore detection of various noise levels in the video frame may signify availability of overlaid text. Hence we exploited this property by detecting regions with various noise levels to localize overlaid text in video frames. Experimental results obtained shows a great improvement in line with overlaid text localization, where we have performed metric measure based on Recall, Precision and f-measure.
Text mining is the process of extracting interesting and non-trivial knowledge or information from unstructured text data. Text mining is the multidisciplinary field which draws on data mining, machine learning, information retrieval, computational linguistics and statistics. Important text mining processes are information extraction, information retrieval, natural language processing, text classification, content analysis and text clustering. All these processes are required to complete the preprocessing step before doing their intended task. Pre-processing significantly reduces the size of the input text documents and the actions involved in this step are sentence boundary determination, natural language specific stop-word elimination, tokenization and stemming. Among this, the most essential and important action is the tokenization. Tokenization helps to divide the textual information into individual words. For performing tokenization process, there are many open source tools are available. The main objective of this work is to analyze the performance of the seven open source tokenization tools. For this comparative analysis, we have taken Nlpdotnet Tokenizer, Mila Tokenizer, NLTK Word Tokenize, TextBlob Word Tokenize, MBSP Word Tokenize, Pattern Word Tokenize and Word Tokenization with Python NLTK. Based on the results, we observed that the Nlpdotnet Tokenizer tool performance is better than other tools.
Automatic Unsupervised Data Classification Using Jaya Evolutionary Algorithmaciijournal
In this paper we attempt to solve an automatic clustering problem by optimizing multiple objectives such as
automatic k-determination and a set of cluster validity indices concurrently. The proposed automatic
clustering technique uses the most recent optimization algorithm Jaya as an underlying optimization
stratagem. This evolutionary technique always aims
to attain global best solution rather than a local best solution in larger datasets. The explorations and exploitations imposed on the proposed work results to
detect the number of automatic clusters, appropriate partitioning present in data sets and mere optimal
values towards CVIs frontiers. Twelve datasets of different intricacy are used to endorse the performance
of aimed algorithm. The experiments lay bare that the conjectural advantages of multi objective clustering optimized with evolutionary approaches decipher into realistic and scalable performance paybacks.
Check out the exclusive range of wall wallpaper we have to offer. Browse through our wide range of wallpapers and pick according to your requirements. Contact us now!
Recommender systems have grown to be a critical research subject after the emergence of the first paper on collaborative filtering in the Nineties. Despite the fact that educational studies on recommender systems, has extended extensively over the last 10 years, there are deficiencies in the complete literature evaluation and classification of that research. Because of this, we reviewed articles on recommender structures, and then classified those based on sentiment analysis. The articles are categorized into three techniques of recommender system, i.e.; collaborative filtering (CF), content based and context based. We have tried to find out the research papers related to sentimental analysis based recommender system. To classify research done by authors in this field, we have shown different approaches of recommender system based on sentimental analysis with the help of tables. Our studies give statistics, approximately trends in recommender structures research, and gives practitioners and researchers with perception and destiny route on the recommender system using sentimental analysis. We hope that this paper enables all and sundry who is interested in recommender systems research with insight for destiny.
Blood groups matcher frame work based on three level rules in myanmaraciijournal
Today Blood donation is a global interest for world to be survival lives when people are in trouble because of natural disaster. The system provides the ability how to decide to donate the blood according to the rules for blood donation not to meet the physicians. In this system, there are three main parts to accept blood from donors when they want to donate according to the features like personal health. The application facilitates to negotiate between blood donors and patients who need to get blood seriously on page without going to Blood Banks and waiting time in queue there.
HYBRID DATA CLUSTERING APPROACH USING K-MEANS AND FLOWER POLLINATION ALGORITHMaciijournal
Data clustering is a technique for clustering set of objects into known number of groups. Several approaches are widely applied to data clustering so that objects within the clusters are similar and objects in different clusters are far away from each other. K-Means, is one of the familiar center based clustering algorithms since implementation is very easy and fast convergence. However, K-Means algorithm suffers from initialization, hence trapped in local optima. Flower Pollination Algorithm (FPA) is the global optimization technique, which avoids trapping in local optimum solution. In this paper, a novel hybrid data clustering approach using Flower Pollination Algorithm and K-Means (FPAKM) is proposed. The proposed algorithm results are compared with K-Means and FPA on eight datasets. From the experimental results, FPAKM is better than FPA and K-Means.
A new algorithm for data compression technique using vlsiTejeswar Tej
HOW COMPRESSION IS POSSIBLE?????????
NOW A DAYS LOT OF ALGORITHMS ARE READY TO COMPRESS DATA BUT POWER IS THE MAJOR CRITERIA OF ALL.BUT MY PROJECT IS TO OVERCOME IT I..E THE NEW ALGORITHM BY
K-RLE
Types of Data compression, Lossy Compression, Lossless compression and many more. How data is compressed etc. A little extensive than CIE O level Syllabus
Erca energy efficient routing and reclusteringaciijournal
The pervasive application of wireless sensor networks (WNSs) is challenged by the scarce energy constraints of sensor nodes. En-route filtering schemes, especially commutative cipher based en-route filtering (CCEF) can saves energy with better filtering capacity. However, this approach suffer from fixed paths and inefficient underlying routing designed for ad-hoc networks. Moreover, with decrease in remaining sensor nodes, the probability of network partition increases. In this paper, we propose energy-efficient routing and re-clustering algorithm (ERCA) to address these limitations. In proposed scheme with reduction in the number of sensor nodes to certain thresh-hold the cluster size and transmission range dynamically maintain cluster node-density. Performance results show that our approach demonstrate filtering-power, better energy-efficiency, and an average gain over 285% in network lifetime.
An Intelligent Method for Accelerating the Convergence of Different Versions ...aciijournal
In a wide range of applications, solving the linear
system of equations Ax = b is appeared. One of the
best
methods to solve the large sparse asymmetric linear
systems is the simplified generalized minimal resi
dual
(SGMRES(m)) method. Also, some improved versions of
SGMRES(m) exist: SGMRES-E(m, k) and
SGMRES-DR(m, k). In this paper, an intelligent heur
istic method for accelerating the convergence of th
ree
methods SGMRES(m), SGMRES-E(m, k), and SGMRES-DR(m,
k) is proposed. The numerical results
obtained from implementation of the proposed approa
ch on several University of Florida standard
matrixes confirm the efficiency of the proposed met
hod.
SURVEY SURFER: A WEB BASED DATA GATHERING AND ANALYSIS APPLICATIONaciijournal
The most important need for a web based survey technology is speedy performance and accurate results
.Though there are variety of ways a survey can be taken manually and have it assessed manually but here
we are introducing the survey site concept where the assessment of the survey responses are created
automatically by applying certain statistics. In this paper we propose the idea of automated approach to
the analysis of the survey where the results would be evident graphically to take a wise decision. Additional
to graphical view we aim on forming a platform to view complex, tedious data in simple, understandable,
interactive and visualized form.
Web spam classification using supervised artificial neural network algorithmsaciijournal
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are more efficient, generic and highly adaptive. Neural Network based technologies have high ability of adaption as well as generalization. As per our knowledge, very little work has been done in this field using neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised learning algorithms of artificial neural network by creating classifiers for the complex problem of latest web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation learning, and Levenberg-Marquardt algorithm.
Learning strategy with groups on page based students' profilesaciijournal
Most of students desire to know about their knowledge level to perfect their exams. In learning environment the fields of study overwhelm on page with collaboration or cooperation. Students can do their exercises either individually or collaboratively with their peers. The system provides the guidelines for students' learning system about interest fields as Java in this system. Especially the system feedbacks information about exam to know their grades without teachers. The participants who answered the exam can discuss with each others because of sharing e mail and list of them.
TRUST METRICS IN RECOMMENDER SYSTEMS: A SURVEYaciijournal
Information overload is a new challenge in e-commerce sites. The problem refers to the fast growing of
information that lead following the information flow in real world be impossible. Recommender systems, as
the most successful application of information filtering, help users to find items of their interest from huge
datasets. Collaborative filtering, as the most successful technique for recommendation, utilises social
behaviours of users to detect their interests. Traditional challenges of Collaborative filtering, such as cold
start, sparcity problem, accuracy and malicious attacks, derived researchers to use new metadata to
improve accuracy of recommenders and solve the traditional problems. Trust based recommender systems
focus on trustworthy value on relation among users to make more reliable and accurate recommends. In
this paper our focus is on trust based approach and discuss about the process of making recommendation
in these method. Furthermore, we review different proposed trust metrics, as the most important step in this
process.
LOCALIZATION OF OVERLAID TEXT BASED ON NOISE INCONSISTENCIESaciijournal
In this paper, we present a novel technique for localization of caption text in video frames based on noise inconsistencies. Text is artificially added to the video after it has been captured and as such does not form part of the original video graphics. Typically, the amount of noise level is uniform across the entire captured video frame, thus, artificially embedding or overlaying text on the video introduces yet another segment of noise level. Therefore detection of various noise levels in the video frame may signify availability of overlaid text. Hence we exploited this property by detecting regions with various noise levels to localize overlaid text in video frames. Experimental results obtained shows a great improvement in line with overlaid text localization, where we have performed metric measure based on Recall, Precision and f-measure.
Text mining is the process of extracting interesting and non-trivial knowledge or information from unstructured text data. Text mining is the multidisciplinary field which draws on data mining, machine learning, information retrieval, computational linguistics and statistics. Important text mining processes are information extraction, information retrieval, natural language processing, text classification, content analysis and text clustering. All these processes are required to complete the preprocessing step before doing their intended task. Pre-processing significantly reduces the size of the input text documents and the actions involved in this step are sentence boundary determination, natural language specific stop-word elimination, tokenization and stemming. Among this, the most essential and important action is the tokenization. Tokenization helps to divide the textual information into individual words. For performing tokenization process, there are many open source tools are available. The main objective of this work is to analyze the performance of the seven open source tokenization tools. For this comparative analysis, we have taken Nlpdotnet Tokenizer, Mila Tokenizer, NLTK Word Tokenize, TextBlob Word Tokenize, MBSP Word Tokenize, Pattern Word Tokenize and Word Tokenization with Python NLTK. Based on the results, we observed that the Nlpdotnet Tokenizer tool performance is better than other tools.
Automatic Unsupervised Data Classification Using Jaya Evolutionary Algorithmaciijournal
In this paper we attempt to solve an automatic clustering problem by optimizing multiple objectives such as
automatic k-determination and a set of cluster validity indices concurrently. The proposed automatic
clustering technique uses the most recent optimization algorithm Jaya as an underlying optimization
stratagem. This evolutionary technique always aims
to attain global best solution rather than a local best solution in larger datasets. The explorations and exploitations imposed on the proposed work results to
detect the number of automatic clusters, appropriate partitioning present in data sets and mere optimal
values towards CVIs frontiers. Twelve datasets of different intricacy are used to endorse the performance
of aimed algorithm. The experiments lay bare that the conjectural advantages of multi objective clustering optimized with evolutionary approaches decipher into realistic and scalable performance paybacks.
Check out the exclusive range of wall wallpaper we have to offer. Browse through our wide range of wallpapers and pick according to your requirements. Contact us now!
Recommender systems have grown to be a critical research subject after the emergence of the first paper on collaborative filtering in the Nineties. Despite the fact that educational studies on recommender systems, has extended extensively over the last 10 years, there are deficiencies in the complete literature evaluation and classification of that research. Because of this, we reviewed articles on recommender structures, and then classified those based on sentiment analysis. The articles are categorized into three techniques of recommender system, i.e.; collaborative filtering (CF), content based and context based. We have tried to find out the research papers related to sentimental analysis based recommender system. To classify research done by authors in this field, we have shown different approaches of recommender system based on sentimental analysis with the help of tables. Our studies give statistics, approximately trends in recommender structures research, and gives practitioners and researchers with perception and destiny route on the recommender system using sentimental analysis. We hope that this paper enables all and sundry who is interested in recommender systems research with insight for destiny.
Blood groups matcher frame work based on three level rules in myanmaraciijournal
Today Blood donation is a global interest for world to be survival lives when people are in trouble because of natural disaster. The system provides the ability how to decide to donate the blood according to the rules for blood donation not to meet the physicians. In this system, there are three main parts to accept blood from donors when they want to donate according to the features like personal health. The application facilitates to negotiate between blood donors and patients who need to get blood seriously on page without going to Blood Banks and waiting time in queue there.
HYBRID DATA CLUSTERING APPROACH USING K-MEANS AND FLOWER POLLINATION ALGORITHMaciijournal
Data clustering is a technique for clustering set of objects into known number of groups. Several approaches are widely applied to data clustering so that objects within the clusters are similar and objects in different clusters are far away from each other. K-Means, is one of the familiar center based clustering algorithms since implementation is very easy and fast convergence. However, K-Means algorithm suffers from initialization, hence trapped in local optima. Flower Pollination Algorithm (FPA) is the global optimization technique, which avoids trapping in local optimum solution. In this paper, a novel hybrid data clustering approach using Flower Pollination Algorithm and K-Means (FPAKM) is proposed. The proposed algorithm results are compared with K-Means and FPA on eight datasets. From the experimental results, FPAKM is better than FPA and K-Means.
Prediction of lung cancer is most challenging problem due to structure of cancer cell, where most of the cells are overlapped each other. The image processing techniques are mostly used for prediction of lung cancer and also for early detection and treatment to prevent the lung cancer. To predict the lung cancer various features are extracted from the images therefore, pattern recognition based approaches are useful to predict the lung cancer. Here, a comprehensive review for the prediction of lung cancer by previous researcher using image processing techniques is presented. The summary for the prediction of lung cancer by previous researcher using image processing techniques is also presented.
Knn a machine learning approach to recognize a musical instrumentIJARIIT
The integrated set of functions written in Matlab, dedicate to the extraction of audio tones of musical options connected
to timbre, tonality, rhythm or type. A study on feature analysis in today’s atmosphere, most of the musical information retrieval
algorithmic programs square measure matter based mostly algorithm so we have a tendency that cannot able to build a
classification of musical instruments. In most of the retrieval system, the classification is often done on the premise of term
frequencies and use of snippets in any documents. We have a tendency to gift MIR tool case, associate degree for recognition of
classical instruments, using machine learning techniques to select and evaluate features extracted from a number of different
feature schemes was described by Deng et al. The performance of Instrument recognition was checked using with different
feature selection and algorithms.
THE INTELLIGENCE OF MACHINES AND BRANCH OF COMPUTER SCIENCE THAT AIMS TO CREATE IT- “The study and design of intelligent agents”.
An area of computer science that deals with giving m/c ability to seem like they have human intelligence.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A new parallel bat algorithm for musical note recognition IJECEIAES
Music is a universal language that does not require an interpreter, where feelings and sensitivities are united, regardless of the different peoples and languages, The proposed system consists of two main stages: the process of extracting important properties using the linear discrimination analysis (LDA) This step is carried out after the initial treatment process using various procedures to remove musical lines, The second stage describes the recognition process using the bat algorithm, which is one of the metaheuristic algorithms after modifying the bat algorithm to obtain better discriminating results. The proposed system was supported by parallel implementation using the (developed bat algorithm DBA), which increased the speed of implementation significantly. The method was applied to 1250 different images of musical notes. The proposed system was implemented using MATLAB R2016a, Work was done on a Windows10 Processor OS (Intel ® Core TM i5-7200U CPU @ 2.50GHZ 2.70GHZ) computer.
Text independent speaker identification system using average pitch and forman...ijitjournal
The aim of this paper is to design a closed-set text-independent Speaker Identification system using average
pitch and speech features from formant analysis. The speech features represented by the speech signal are
potentially characterized by formant analysis (Power Spectral Density). In this paper we have designed two
methods: one for average pitch estimation based on Autocorrelation and other for formant analysis. The
average pitches of speech signals are calculated and employed with formant analysis. From the performance
comparison of the proposed method with some of the existing methods, it is evident that the designed
speaker identification system with the proposed method is superior to others.
Voice recognition system is a system which is used to convert human voice into signal, which can be understood by the machines. When this is achieved, the machine can be made to work, as desired. The machine could be a computer, a typewriter, or even a robot. There are systems available, in which the machine ‘speaks’ the recorded word. But that is out of the scope of this paper. Here, only the human is expected to talk. Further, the voice recognition systems described here, can be used for projects only.
In this paper we present the implementation of speaker identification system using artificial neural network
with digital signal processing. The system is designed to work with the text-dependent speaker
identification for Bangla Speech. The utterances of speakers are recorded for specific Bangla words using
an audio wave recorder. The speech features are acquired by the digital signal processing technique. The
identification of speaker using frequency domain data is performed using backpropagation algorithm.
Hamming window and Blackman-Harris window are used to investigate better speaker identification
performance. Endpoint detection of speech is developed in order to achieve high accuracy of the system.
Similar to Audio Signal Identification and Search Approach for Minimizing the Search Time in Audio Fingerprinting Using Template Matching (20)
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Audio Signal Identification and Search Approach for Minimizing the Search Time in Audio Fingerprinting Using Template Matching
1. Advanced Computational Intelligence: An International Journal (ACII), Vol.3, No.2, April 2016
DOI:10.5121/acii.2016.3203 23
AUDIO SIGNAL IDENTIFICATION AND SEARCH
APPROACH FOR MINIMIZING THE SEARCH TIME IN
AUDIO FINGERPRINTING USING TEMPLATE
MATCHING
Er. Mohammed Ahmed1
1
Computer Engineering Department
M.H. Saboo Siddik College of Engineering
Mumbai, India
Swaleha Shaikh2
, Hera Ansari3
and Arshi Siddiqui4
2,3,4
Department of Computer Engineering, M.H.Saboo Siddik College Of Engineering
ABSTRACT
Audio or music fingerprints can be utilize to implement an economical music identification system on a
million-song library, however the system needs great deal of memory to carry the fingerprints and indexes.
Therefore, for a large-scale audio library, memory imposes a restriction on the speed of music
identifications. So, we propose an efficient music identification system which used a kind of space-saving
audio fingerprints. For saving space, original finger representations are sub-sample and only one quarters
of the original data is reserved. In this approach, memory demand is far reduced and therefore the search
speed is criticalincreasing whereas the lustiness and dependability ar well preserved. Mapping
audio information to time and frequency domain for the classification, retrieval or identification tasks
presents four principal challenges. The dimension of the input should be considerably reduced;
the ensuing options should be strong to possible distortions of the input; the feature should be informative
for the task at hand simple. We propose distortion free system which fulfils all four of these requirements.
Extensive study has been done to compare our system with the already existing ones, and the results show
that our system requires less memory, provides fast results and achieves comparable accuracy for a large-
scale database.
KEYWORDS
Audio Fingerprint, Fingerprint Extraction, Audio Template, Fast Fourier transform, Database Search.
1. INTRODUCTION
Nowadays, due to rapid digitization, digital music libraries are becoming popular and
expanding[1][2][3][4]. With their increasing demand it has become essential to study them.
The potency to seek out the precise song from outsized audio information is especially a vital task
for variety of application. Fingerprints are short summaries of multimedia content. It is similar to
Human fingerprint that is used for identifying an individual; an audio fingerprint is used for
matching an audio clip. The modified version of the audio should have the similar fingerprints
with the original or real audio. Various systems have been developed consisting of different
techniques and methods. Each of it had some or the other drawback’s[5][6][7][8][9][10][11].
The audio fingerprinting technique has many applications in broadcasts monitoring, automatic
generations of playlist of radio, TV, or web broadcast etc. Another application can be described
as search (retrieving) the name of the artist and title of the songs given a short clip of an audio,
2. Advanced Computational Intelligence: An International Journal (ACII), Vol.3, No.2, April 2016
24
filtering technology for files sharing and so on. Since an audio contains many properties,
extracting a sample of the audio fingerprint is a potential task that identifies each song uniquely.
Figure 1: Overview of Audio signal identification
A fingerprinting system which is used for identification is generally made up of the following
main modules:
1. Fingerprint extraction-This involves removing the unique characteristic of each audio
input.
2. DB search-In this step, the audio is searched with the already stored audio in the song
Meta data.
3. Fingerprint matching-Finally, the fingerprints are compared. If there exists a match of
fingerprints then respective result is returned.
Our main focus is on how to increase the speed of searching as well as decrease the searching
time by a considerable amount.
Broadly speaking, the formula may be logically rotten into 2 main parts[17]:
• Fingerprint creation (extracting distinctive sensory activity options from the song) and
• Fingerprint search (querying the database)
Because many elements are concerned in each activity, they'll be summarized along.
2. PROPOSED SYSTEM
The proposed search algorithm contains two phases as described below:
i. In the initial phase, all those songs are searched and retrieved which could probably
contain the query songs fingerprint.
ii. In the second major phase, the entire fingerprint of each matched songs is retrieved from
the database and is compared with the query fingerprint that is in question, if the song
matches with the query fingerprint then it is retrieved.
In simple terms, if you want to compare audio or music files by their perceptual equality, you
should create the so called "fingerprints" (same or similar to human fingerprints, which uniquely
describe a person's identity), and sees if sets of these objects, gathered from different audio or
music sources match or not. Logically, similar audio objects should generate similar fingerprints,
whereas different file should emanate unlike signatures. A pre requisite for these fingerprints is
that they should act as "hash", in order with format differences, difference in frequencies,
3. Advanced Computational Intelligence: An International Journal (ACII), Vol.3, No.2, April 2016
25
distortions, noise, "intensity", etc. The simplified concepts of audio fingerprinting can be
visualized below.
Figure 2: Modules in the proposed system
• Fingerprint as a hash value
Briefly, audio fingerprint may be seen as a compressed outline of the corresponding audio object.
Mathematically speaking, a finger mark operate F maps the audio object X consisting from an
oversized variety of bits to a fingerprint of solely a restricted variety of bits. So, the complete
mechanism of extraction of a fingerprint may be seen as a hash-function H, that maps Audio
object X to a hash (a.k.a. message digest). The most advantage why hash functions are therefore
widely utilized in the sphere of engineering science is that they permit scrutiny 2 massive objects
X, Y; by simply scrutiny their individual hash values H(X), H(Y). The equality between latter
pairs implies the equality between X, Y, with a really low error chance (even although collision
cannot be eliminated entirely). Next, you'll be able to see an abstract diagram of the hash
function's practicality.
Figure 3: Conceptual diagram of the hash function's functionality
4. Advanced Computational Intelligence: An International Journal (ACII), Vol.3, No.2, April 2016
26
Here within the figure, string values act as keys, mapping into hash bins (integers) that are
smaller purposely and far easier to check. Nonetheless, there's forever little chance that fully
completely different keys can occupy a similar bin (Steve and Mark collision). So, why will we
get to develop a completely new system supported fingerprints, and not simply use crypto logic
hash functions? The solution is pretty straightforward, though the sensory activity distinction of
audio object compressed to MP3 format and therefore the same one compressed to WAVE is
incredibly little, the binary illustration of these are altogether completely different, that means
that H(MP3(X)) and H(WAVE(X)) can end in fully dissimilar message digests (hash bins). Even
worst, crypto logic hash functions are typically terribly sensitive: one little bit of distinction
within the original object ends up in {a completely an altogether a very} different totally
completely different completely different} hash price (thus a little degradation within the key can
result in totally different hash digest). So we propose a system which will not take under
consideration any low level details (binary signatures) and can analyse solely the audio signal as
any human will (will act as a "forgiving hash" that will not pay abundant attention to irrelevant
differences).
Thus, audio fingerprinting algorithms have the potential to link small, unknown chunks of audio
templates i.e., finding similar properties of a song that can be afterwards used to recall a new
unknown sample, or find a duplicate in the database of already processed songs. These types of
algorithms can be used in a variety of different application:
i. Identifying the currently playing song
ii. Managing sound effects libraries
iii. Observing and controlling the broadcast monitoting[2][3][4[5][12].
With all the advantages that the sounds fingerprinting system has, there are several challenge that
it has to address. One of them is a huge database to searches (imagine YouTube's database of
video content that is monitors for audio copyright issues). Normally, each song will generates a
big amount of fingerprints (in the described algorithms , the granularity of each of them is going
to be 1.48 sec, so we'll have about 200 object for a song). Lastly, there will be a requirement for
developing an economical yet efficient search rule that works well within scalable solution
bounds.
1. IMPLEMENTATION
The framework that goes to be designed (upon that the complete system can reside) can
comprises many completely different abstract elements. Within the next figure, you'll be able to
visualize the activity diagram that abstractly describes the logical flow of the audio fingerprint
generation. Following, can describe in deeper details every activity concerned and what element
is accountable for it.
5. Advanced Computational Intelligence: An International Journal (ACII), Vol.3, No.2, April 2016
27
.
Figure 4: Phases involved in Fingerprint extraction algorithm
4. PREPROCESSING THE INPUT SIGNAL
Merely, all audio files that anyone has on his laptop are encoded in some format. Therefore, the
primary step (1) within the formula is decipherment the input data to PCM (Pulse Code
Modulation format). The PCM format may be thought of the raw digital format of analog signal,
within which the magnitude of the signal is sampled often at uniform intervals. For time-domain
signals, the unit for rate is 1/s. The inverse of the frequency is that the sampling amount or
sampling interval, that is that the time between samples. because the analysed band within the
formula lies inside the vary of 318 Hz and 2000 Hz, down sampling the input to 5512 Hz is taken
into account a secure and, at a similar time, a needed operation. Whereas reducing the sample rate
(normally from 44100 Hz), we'll get eliminate data that isn't relevant from the human sensory
activity purpose of read, and can specialize in vital options of the signal. The pre-processing is
finished victimization the Bass.Net library (in my opinion, the simplest one for operating with
audio files). With the refashion of the Sound Fingerprinting framework, you'll be able to use
library for the needs printed higher than. The sole bother with Audio is that you simply do not
have that abundant flexibility in terms of supported audio formats.
6. Advanced Computational Intelligence: An International Journal (ACII), Vol.3, No.2, April 2016
28
Figure 5: Input Signal Processing
5. SPECTROGRAM CREATION
After pre-processing the signal to 5512 cycles/second PCM, successive (2) step within the
procedure algorithmic rule is building the spectrograph of the audio input. In digital signal
process, a spectrograph is a picture that shows however the spectral density of a symbol varies in
time. Changing the signal to spectrograph[6] involves many steps. 1st of all, the signal ought to
be sliced into overlapping frames. Every frame ought to then be skilled a quick Fourier remodel
so as to urge the spectral density varied in time domain. The parameters utilized in these
transformation steps are going to be adequate those who are found to figure well in different
audio procedure studies (specifically in an exceedingly extremely sturdy Audio procedure
System): audio frames that area unit 371 Ms long (2048 samples), taken each 11.6 Ms (64
samples), so having associate overlap of 31/32, as have been used in the previous study. H an k
2002 ref
Figure 6: Spectrogram of the audio input
Building the spectrograph of the image involves the foremost high-ticket (from a central
processing unit purpose of view) operation within the entire algorithmic rule - quick Fourier
remodel (O (n*log (n))).Fourier analysis could be a family of mathematical techniques, all
supported rotten signals into sinusoids.
7. Advanced Computational Intelligence: An International Journal (ACII), Vol.3, No.2, April 2016
29
Figure 7: Forward / Inverse Fourier Transform
6. BAND FILTERING
After the FFT remodel is accomplished on every slice, the output spectrograph is cut such 318Hz-
2000Hz domain is obtained (researchers set that this domain is enough for a relevant illustration
of the sensory activity content of a file). Generally, this domain is thought of to be one among the
foremost relevant frequency areas for Human sensory system. whereas the vary of frequencies
that any person will hear is basically associated with environmental factors, the commonly
accepted customary vary of perceptible frequencies is twenty to 20000Hz. Frequencies below
20Hz will typically be felt instead of detected, assumptive the amplitude of the vibration is high
enough. Frequencies on top of 20000Hz will generally be detected by youth, however high
frequencies area unit the primary to be plagued by hearing disorder as a result of age and/or
prolonged exposure to terribly loud noises. Within the next table, you'll be able to visualize the
frequency domains and their main descriptions. Because it is seen, the foremost relevant audio
content lies inside the 512-8192Hz frequency vary, because it defines traditional speech.
7. WAVELET DECOMPOSITION:
Once the power spectrograph is "drawn" (having thirty two bins contact the 318-2000Hz vary, for
every eleven.6 MS), successive step within the algorithmic rule (4) is dividing the whole image
into slices (spectral sub-images one28x32) that correspond to a 1.48 sec roughness. In terms of
programming, once you build the log-spectrogram, you bought its entire image encoded into
associate [N] [32] double-dimensional float array, wherever N equals the "length of the signal in
milliseconds divided by eleven.6 MS", and thirty two represents the amount of frequency bands.
Once you get the divided spectral sub-images, they're going to be more reduced by applying
customary Haar[7] ripple decomposition on every of them. The utilization of wavelets within the
audio-retrieval task is finished as a result of their flourishing use in image retrieval (Fast
Multiresolution Image Querying). In different words, a ripple could be a wave-like oscillation
with amplitude that starts at zero, increases, and so decreases back to zero. It will generally be
unreal as a "brief oscillation" just like the one seen on a measuring device or monitor.
8. Advanced Computational Intelligence: An International Journal (ACII), Vol.3, No.2, April 2016
30
Figure 8: Framework for Audio-retrieval task
Wavelets area unit crafted purposefully to possess specific properties that build them helpful for
signal/image process. They’ll be combined by employing a "shift, multiply and sum" technique
referred to as convolution, with parts of associate unknown signal to extract info from this signal.
The elemental plan behind wavelets is to investigate in step with scale. So, turning back to our
algorithmic rule, for every spectral picture, a ripple signature is computed. Rather than
exploitation the whole set of wavelets, we have a tendency to solely keep those that almost all
characterize the song (top two hundred is taken into account an honest choice), so creating the
signature proof against noise and different degradation. One among the fascinating things found
within the previous studies of image process was that there's no have to be compelled to keep the
magnitude of high wavelets; instead, we are able to merely keep the sign of it (+/-). This info is
enough to stay the extract sensory activity characteristics of a song.
8. MIN HASH THE FINGERPRINT
At this stage of process, we've got fingerprints that area unit 8192 bits log, that we'd wish to more
cut back in their length, by making a compact illustration of every item. Therefore, within the
next step (5) of the algorithmic rule, we have a tendency to explore the utilization of Min-
Hash[16] to reason sub-fingerprints for these distributed bit vectors. Its usage has proved to be
effective within the economical search of comparable sets. It works as follows: think about a
column as a group of rows during which the column contains a one (consider C1 and C2 as 2
fingerprints). Then the similarity of 2 columns C1 andC2 is Sim (C1, C2) = (C1∩C2)/(C1∪C2).
This similarity index is named the Jaccard similarity: it's variety between zero and 1; it's zero
once the 2 sets area unit disjoint, one once they area unit equal, and strictly between zero and one
otherwise. Before exploring however Min-Hash works, I am going to purpose to an awfully
helpful characteristic that it has: the likelihood that MinHash (C1) = MinHash (C2) equals
specifically to Jaccard similarity. this can permit U.S. to use the algorithmic rule by with
efficiency hashing similar things (with a similarity index over 60-70%) into identical bins
(exactly what we have a tendency to have an interest in - "forgiving hashing" technique that will
not pay a lot of attention to little variations between 2 keys). The Min-Hash technique works with
binary vectors as follows: choose a random, but known, rearrangement of all the vector positions.
Then, for every vector permutation live during which position the primary '1' happens. As a result
of the system can work with fingerprints of 8192 bit size, the permutation vector can contain
random indexes ranging from zero to 8192. For a given permutation, the hash values agree if the
primary position with a one is that the same in each bit vectors, and that they disagree if the
primary such position could be a row wherever one, however not each, vectors contained a one.
Note that this can be specifically what's required; it measures the similarity of the distributed bit
vectors supported matching "on" positions. We are able to repeat the on top of procedure multiple
9. Advanced Computational Intelligence: An International Journal (ACII), Vol.3, No.2, April 2016
31
times, on every occasion with a brand new position-permutation. If we have a tendency to repeat
the method p times, with p totally different permutations, we have a tendency to get p for the
most part freelance projections of the bit vect (e.g. p=100permutations).
Figure 9: Min-Hash to compute sub-fingerprints
9. RETRIEVAL
After the min-hashed fingerprint is partitioned off into LSH tables, the LSH technique is efficient
in the number of comparisons that are required and provides noise-robustness properties[13]. The
method of extracting signatures from audio files is thought of completed. Now, however will we
discover similar songs or retrieve info regarding associate unknown audio by having solely a tiny
low piece of it? The retrieval method (detection of the question song) is analogous to the
creational one, with associate exception that not the whole song is fingerprinted at now. You will
choose as several seconds to fingerprint as you wish, creating an exchange between accuracy and
interval. The steps area unit recurrent until the fingerprint is partitioned off into hash tables. Once
done, the question is shipped to the storage to envision what percentage hash tables contain
corresponding hash values. If over v votes are unit non heritable throughout the question (more
than v tables have come back identical fingerprint), the fingerprint is taken into account a possible
match (in Content procedure exploitation wavelets, a threshold of 2-5 votes was used). All
matches area unit elite and analysed so as to search out most effective} candidate (a simple
overacting distance calculation between the question and potential candidates is exhausted order
to envision what item is that the most relevant one). Just in case of developing a duplicates
detector algorithmic rule, the principle of extraction could be a bit totally different. Range of
threshold votes is inflated to eight (8/25 tables ought to come identical result), and a minimum of
five-hitter of total question fingerprints ought to vote for identical song. Once this criterion is
met, the song is taken into account a reproduction. Overall, of these parameters area unit outlined
through empirical observation, thus you may notice even higher configuration which will work
additional correct because the one delineated here. As associate example, you will lower the edge
votes so as to get results which will additionally contain the remixes of the songs matched as
duplicates (it's perpetually a trade-off between false-positives and therefore the objective
function).
10. Advanced Computational Intelligence: An International Journal (ACII), Vol.3, No.2, April 2016
32
10. FUTURE SCOPE
Here in this paper, an alternative searching method has been elaborated. On comparison with the
older schemes a significant amount of decrease in the audio retrieval was noticed. On one hand
making use of hash function provided uniqueness to each song so on the other hand,
fingerprinting on only required length of song saved a large amount of disk space. The above
scheme has a scope and can be applied to areas of military and telecommunications. Also big
music industries such as radio stations, airtel music stores, online song sites etc. can make use of
this to avoid repetitive data and thereby save space. Further it can be used by making it for sppech
datasets and for variety of other audio formats also.
CONCLUSION
In this paper, we propose a new and efficient process for environments where large audio
databases need to be systemized and non-redundant. The fingerprinting scheme theoretically
gives better results for large databases and saves time. Also only small fragments of each song
fingerprint is created which saves time and reduces the memory requirement. This scheme gives
more emphasis on extracting unique fingerprint value and avoids the other distortions or
dissimilarity such as difference in genres, pitch[14], level of noise content, zero cross rating[15]
etc. present in the audio. Also this scheme will avoid redundancy of songs existing in different
files, folders or even root directories. Fingerprint along with hashing provides optimal and
efficient search results.
ACKNOWLEDGEMENT
No project can be completed without the support of a lot of people. When we are concluding our
project synopsis work by submitting this report, we reflect upon all the times when we needed
support in various forms and were lucky enough to receive it.
We wish to express our sincere gratitude to our principal Dr. Mohiuddin Ahmed, M. H. Saboo
Siddik College of Engineering, Mumbai, for providing the facilities to carry out the project work.
A special mention here to Prof. Z. A. Usmani (H.O.D., Computer Engineering Department,
MHSSCOE) for his valuable support. We are also thankful to all staff members of Computer
Department, without whom the completion of this report would have been impossible.
This entire journey would not have been possible without the efforts put in by our guide, Er.
Mohd Ahmed. They have been a constant source of encouragement and guidance through the
entire semester.
This acknowledgment would indeed be incomplete without rendering our sincere gratitude to our
family. They have always been a pillar of strength and support in our past and current endeavors.
REFERENCES
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Music Information Retrieval (ISMIR) 2001, Bloomington, USA, October 2001.
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Content-Based Identification of Audio Material”, 100th AES Convention, Amsterdam, The
Netherlands, May 2001.
11. Advanced Computational Intelligence: An International Journal (ACII), Vol.3, No.2, April 2016
33
3. Neuschmied H., Mayer H. and Battle E., “Identification of Audio Titles on the Internet”,
Proceedings of International Conference on Web Delivering of Music 2001, Florence, Italy,
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4. Fragoulis D., Rousopoulos G., Panagopoulos T., Alexiou C. and Papaodysseus C., “On the
Automated Recognition of Seriously Distorted Musical Recordings”, IEEE Transactions on Signal
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5. J.S. Seo, J. Haitsma, T. Kalker, and C.D. Yoo, “A robust image fingerprinting system using the
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Author
Mohd. Ahmed is currently Assistant professor at the M.H. Saboo
Siddik College of Engineering and pursuing Phd, Mumbai and formerly
trainee hardware & network engineer in DSS Mobile communication,
Mumbai. He is a M. E. in Computer Engineering. Having 11 years
teaching and one year industry experience.
Shaikh Swaleha has completed Diploma in computer engineering from
M.H. Saboo Siddik College of Engineering and currently pursuing B.E
in computer engineering
Ansari Hera has completed Diploma in computer engineering from
Babasaheb Gawde Institute of Technology and currently pursuing B.E
in computer engineering.
Siddiqui Arshi has completed Diploma in computer engineering from
Babasaheb Gawde Institute of Technology and currently pursuing B.E
in computer engineering.