M. M. Dialin

Bio: M. M. Dialin is an academic researcher from Mepco Schlenk Engineering College. The author has contributed to research in topics: Speech coding & Audio mining. The author has an hindex of 1, co-authored 1 publications receiving 10 citations.

Fingerprint extraction of audio signal using wavelet transform

Abstract: Due to the massive growth in computer technology enormous amount of music files are shared in the internet. The storage devices of music files are also changed from cassette tapes to MP3 players, mobile phones, DVD players. In order to solve problems in searching the needed songs among mass audio information, digital audio fingerprinting is involved. Audio fingerprint is a content based compact signature, which summarizes the audio. Fingerprinting systems extracts the acoustic characteristics of the audio and store it as a fingerprint and store it in a database. When the unknown, short snippet of an audio given as query, it calculates its characteristics and compared it to the stored database. Using the fingerprint matching algorithms, the particular audio data was successfully identified even if the audio data has been distorted severely. However the size of the fingerprint per music file is increased, then the amount of songs with their fingerprints in the database is less. So, a novel method proposed to reduce the size of the fingerprints thereby increasing the amount of songs stored in the database. The proposed method uses the wavelet transform for extracting the features so it is effectively used for identification even if the audio quality of the audio has been changed and the amount of fingerprints generated also less.

Face Recognition Using Sparse Fingerprint Classification Algorithm

Abstract: Unconstrained face recognition is still an open problem as the state-of-the-art algorithms have not yet reached high recognition performance in real-world environments. This paper addresses this problem by proposing a new approach called sparse fingerprint classification algorithm (SFCA). In the training phase, for each enrolled subject, a grid of patches is extracted from each subject’s face images in order to construct representative dictionaries. In the testing phase, a grid is extracted from the query image and every patch is transformed into a binary sparse representation using the dictionary, creating a fingerprint of the face. The binary coefficients vote for their corresponding classes and the maximum-vote class decides the identity of the query image. Experiments were carried out on seven widely-used face databases. The results demonstrate that when the size of the data set is small or medium ( e.g. , the number of subjects is not greater than one hundred), SFCA is able to deal with a larger degree of variability in ambient lighting, pose, expression, occlusion, face size, and distance from the camera than other current state-of-the-art algorithms.

QuickAuth: Two-Factor Quick Authentication Based on Ambient Sound

Abstract: Authentication is the first step to access a resource (service, website, data, etc.), so it is of vital importance in a system. The most widely used authentication mechanisms are one-factor authentication based on password and two-factor authentication methods which require a password and another factor (verification code, biometric feature, hardware token, software plug-in, etc.). However, in many public areas, passwords may be exposed to other monitoring equipments or even other people; while authentication for the second factor always requires human-machine interaction. As a result, password might suffer misuse by others; the latter may need extra procedures and incur long delay, decreasing usability and deployability of the system. In this paper, we propose QuickAuth, which aims to develop a simple, yet quick authentication method that does not rely on password such that the entire authentication procedure can be implemented automatically with minimal user involvement. In QuickAuth, one authentication factor is the user's cellphone, and the other is the proximity of the cellphone to the computer which the user wants to login to. The proximity of the two devices is obtained by comparing ambient sound recorded by microphones. Through the real-world implementation and evaluation, we show that QuickAuth works well in terms of usability, deployability, and security in the environment of public areas.

A Music Identification System Based on Audio Fingerprint

Abstract: Music recognition is more and more widely used in our life. This paper presents a music identification system based on audio fingerprinting. In this paper, a set of music recognition system based on the audio fingerprint is designed, which is based on the audio fingerprint, and constructs the audio fingerprint database to realize the function application of the target music recognition. The system firstly encodes and decodes the audio file and then obtains the time-frequency spectrogram. Then extracts the peak feature points and maps the samples to the fingerprint.Establishes the fingerprint database and identifies the targets in the last. The experimental results show that the system has strong robustness and high recognition rate in music recognition.

Fast content-based audio retrieval algorithm

Abstract: Fingerprinting is one of the most used techniques for searching and identification audio with a wide spectrum of applications. Different algorithms defines different fingerprint extraction and the match techniques, with different efficiency, computational load, robustness, response time and location search. Nowadays music audio retrieval faces two main challenges in order to be efficient: robustness and speed. This article proposes a fast algorithm to the audio content-based retrieval with the fingerprint technique, based on the extraction of the frequency features of the audio and a hash function. Experiments determined a high success rate and a response time lower than other techniques, optimal to real time applications like monitoring radio stations or songs identifying.

Landmark-based music recognition system optimisation using genetic algorithms

Abstract: Audio fingerprinting allows us to label an unidentified music fragment within a previously generated database. The use of spectral landmarks aims to obtain a robustness that lets a certain level of noise be present in the audio query. This group of audio identification algorithms holds several configuration parameters whose values are usually chosen based upon the researcher's knowledge, previous published experimentation or just trial and error methods. In this paper we describe the whole optimisation process of a Landmark-based Music Recognition System using genetic algorithms. We define the actual structure of the algorithm as a chromosome by transforming its high relevant parameters into various genes and building up an appropriate fitness evaluation method. The optimised output parameters are used to set up a complete system that is compared with a non-optimised one by designing an unbiased evaluation model.