Audio Retrieval Using Multiple Feature Vectors

Content Based Audio Retrieval system is very helpful to facilitate users to find the target audio materials. Audio signals are classified into speech, music, several types of environmental sounds and silence based on audio content analysis. The extracted audio features include temporal curves of the average zero-crossing rate, the spectral Centroid, the spectral flux, as well as spectral roll-off of these curves. In this dissertation we have used the four features for extracting the audio from the database, use of this multiple features increase the accuracy of the audio file which we are retrieving from the audio database

Landmark Based Audio Fingerprinting for Naval Vessels

This paper presents a novel landmark based audio fingerprinting algorithm for matching naval vessels' acoustic signatures. The algorithm incorporates joint time - frequency based approach with parameters optimized for application to acoustic signatures of naval vessels. The technique exploits the relative time difference between neighboring frequency onsets, which is found to remain consistent in different samples originating over time from the same vessel. The algorithm has been implemented in MATLAB and trialed with real acoustic signatures of submarines. The training and test samples of submarines have been acquired from resources provided by San Francisco National Park Association [14]. Storage requirements to populate the database with 500 tracks allowing a maximum of 0.5 Million feature hashes per track remained below 1GB. On an average PC, the database hash table can be populated with feature hashes of database tracks @ 1250 hashes/second achieving conversion of 120 seconds of audio data into hashes in less than a second. Under varying attributes such as time skew, noise and sample length, the results prove algorithm robustness in identifying a correct match. Experimental results show classification rate of 94% using proposed approach which is a considerable improvement as compared to 88% achieved by [17] employing existing state of the art techniques such as Detection Envelope Modulation On Noise (DEMON) [15] and Low Frequency Analysis and Recording (LOFAR) [16]

A quick search method for audio signals based on a piecewise linear representation of feature trajectories

This paper presents a new method for a quick similarity-based search through long unlabeled audio streams to detect and locate audio clips provided by users. The method involves feature-dimension reduction based on a piecewise linear representation of a sequential feature trajectory extracted from a long audio stream. Two techniques enable us to obtain a piecewise linear representation: the dynamic segmentation of feature trajectories and the segment-based Karhunen-L\'{o}eve (KL) transform. The proposed search method guarantees the same search results as the search method without the proposed feature-dimension reduction method in principle. Experiment results indicate significant improvements in search speed. For example the proposed method reduced the total search time to approximately 1/12 that of previous methods and detected queries in approximately 0.3 seconds from a 200-hour audio database.Comment: 20 pages, to appear in IEEE Transactions on Audio, Speech and Language Processin

QUERY CLIP GENRE RECOGNITION USING TREE PRUNING TECHNIQUE FOR VIDEO RETRIEVAL

ABSTRACT Optimal efficiency of the retrieval techniques depends on the search methodologies that are used in the data retrieving system. The use of inappropriate search methodologies may make the retrieval system ineffective. In recent years, the multimedia storage grows and the cost for storing multimedia data is cheaper. So there is huge number of videos available in the video repositories. It is difficult to retrieve the relevant videos from large video repository as per user interest. Hence, an effective video and retrieval system based on recognition is essential for searching video relevant to user query from a huge collection of videos. An approach, which retrieves video from repository by recognizing genre of user query clip is presented. The method extracts regions of interest from every frame of query clip based on motion descriptors. These regions of interest are considered as objects and are compared with similar objects from knowledge base prepared from various genre videos for object recognition and a tree pruning technique is employed to do genre recognition of query clip. Further the method retrieves videos of same genre from repository. The method is evaluated by experimentation over data set containing three genres i.e. sports movie and news videos. Experimental results indicate that the proposed algorithm is effective in genre recognition and retrieval

Quick Audio Retrieval Using Multiple Feature Vector

The types of information are changed text-based into various multimedia data such as speech, image, and moving picture. Therefore, it is necessary to study about searching algorithm. Previous keyword-based retrieval is not optimal for searching the multimedia data. Therefore, the studying is focus on the content-based retrieval (etc. MPEG-7) has been attracted. This thesis concentrated on the content-based retrieval and proposed a quick search method. In the Audio Information Retrieval (AIR) System, it is important to extract feature vectors. Feature extraction is the process of computing a numerical representation that can be used to characterize a segment of audio. In this thesis, we use the features based on the Short Time Fourier Transform (STFT) and the zero-crossing rates. Firstly, Features based on the STFT are very common and have the advantage of fast calculation based on the Fast Fourier Transform algorithm. The STFT features can be classified into the spectral centroid, the spectral roll-off and the spectral flux. In the second place, the zero-crossing features have been used in the previous papers because of reducing the computation. This thesis also proposes a new search using the preprocessing and code matching. The previous papers propose a time-series search method using the upper bound proof. It is assumed that similarity between the test and reference template shows considerable correlation from one time step to the next. Because the search algorithm using the upper bound proof computes upper bound on the similarity measures, this method can make possible the quick search. However the search speed of a time-series search method is very low at real time. Therefore this thesis proposes a method using the preprocessing to make up for this defect. Furthermore, we use the code matching method to reduce the matching rates. This thesis is organized as follows : Section 2 overviews the previous time-series search algorithm. Section 3 explains the core part of our new algorithm and the new optimal combination of multiple features. Section 4 evaluates the accuracy and speed of the algorithm using multiple features. Finally Section 5 gives conclusions and future works.제 1 장 서 론 1 제 2 장 오디오 검색 과정 5 2.1 특징 벡터 추출 6 2.1.1 Zero Crossing Rate (ZCR) 7 2.1.2 STFT에 기반을 둔 특징 벡터 8 2.2 히스토그램 모델링과 유사도 측정 11 2.3 window skipping 11 2.4 시간 순서 오디오 검색의 단점 15 제 3 장 다중 특징 벡터를 이용한 고속 오디오 검색 16 3.1 다중 특징 벡터 구성 18 3.1.1 다중 특징 벡터 조합의 정확도 비교 20 3.1.2 다중 특징 벡터 조합의 처리 시간 비교 25 3.1.3 다중 특징 벡터의 조합 26 3.2 유사도 측정 28 3.2.1 여러 가지 유사도 측정 방법 30 3.3 제안한 고속 오디오 검색의 알고리즘 34 제 4 장 실험 과정 및 결과 35 4.1 검색의 정확도 35 4.2 검색 속도 37 제 5 장 결 론 39 참고문헌 4

Efficient video identification based on locality sensitive hashing and triangle inequality

Master'sMASTER OF SCIENC