@article {645,
	title = {Application-Programming Interface (API) for Song Recognition Systems},
	journal = {Advances in Science Technology and Engineering Systems Journal },
	volume = {6},
	year = {2021},
	month = {04/2021},
	pages = {14},
	chapter = {846},
	abstract = {<p>The main contribution of this paper is the framework of Application Programming Interface (API) to be integrated on a smartphone app. The integration with algorithm that generates fingerprints from the method ST-PSD with several parameter configurations (Windows size, threshold, and sub-score linear combination coefficient). An approach capable of recognizing an audio piece of music with an accuracy equal to 90\% was further tested based on this result. In addition the implementation is done by algorithm using Java{\textquoteright}s programming language, executed through an application developed in the Android operating system. Also, capturing the audio from the smartphone, which is subsequently compared with fingerprints, those present in a database.</p>
},
	keywords = {Database Clustering, IoT, Mobile API, Signal Processing, Song Recognition},
	issn = {2415-6698},
	doi = {10.25046/aj060298},
	author = {M. Arif Bin Sahbudin and Chaouch, Chakib and Serrano, Salvatore and Scarpa, Marco}
}
@article {sahbudin2020PSD,
	title = {Audio Fingerprint based on Power Spectral Density and Hamming Distance Measure},
	journal = {Journal of Advanced Research in Dynamical and Control Systems},
	volume = {12},
	number = {04-Special Issue},
	year = {2020},
	pages = {1533{\textendash}1544},
	keywords = {own},
	author = {Sahbudin, Murtadha Arif Bin and Chaouch, Chakib and Scarpa, Marco and Serrano, Salvatore}
}
@article {sahbudin2020kmodes,
	title = {Audio Fingerprint Database Structure using K-modes Clustering},
	journal = {Journal of Advanced Research in Dynamical and Control Systems},
	volume = {12},
	number = {04-Special Issue},
	year = {2020},
	pages = {1545{\textendash}1554},
	keywords = {own},
	author = {Chaouch, Chakib and Sahbudin, Murtadha Arif Bin and Scarpa, Marco and Serrano, Salvatore}
}
@article { 11570_3131282,
	title = {GSM-RF Channel Characterization Using a Wideband Subspace Sensing Mechanism for Cognitive Radio Networks},
	journal = {WIRELESS COMMUNICATIONS AND MOBILE COMPUTING},
	volume = {2018},
	year = {2018},
	pages = {1{\textendash}11},
	abstract = {In this paper, we examine a spectrum sharing opportunities over the existing Global System of Mobile Communication (GSM) networks, by identifying the unused channels at a specific time and location. For this purpose, we propose a wideband spectrum sensing mechanism to analyze the status of 51 channels at once, belonging to the 10 MHz bandwidth centered at the frequency 945 MHz, in four different areas. We propose a subspace based spectral estimation mechanism, adapted to deal with real measurements. The process begins with data collection using Secondary User (SU) device enabled with Software Defined Radio (SDR) technology, configured to operate in the GSM band. Obtained samples are used then to feed the sensing mechanism. Spectral analysis is delivered to estimate power density peaks and corresponding frequencies. Decision making phase brings together power thresholding technique and GSM control channel decoding to identify idle and busy channels. Experiments are evaluated using detection and false alarm probabilities emulated via Receiver Operating Characteristic (ROC) curves. Obtained performances show better detection accuracy and robustness against variant noise/fading effects, when using our mechanism compared to Energy Detection (ED) based ones as Welch method, and Beamforming based ones as Minimum Variance Distortionless Response (MVDR) method. Occupancy results exhibit considerable potential of secondary use in GSM based primary network.},
	keywords = {Cognitive Radio Networks, GNU-Radio, GSM, USRP, Wideband Subspace Sensing},
	doi = {10.1155/2018/7095763},
	url = {https://www.hindawi.com/journals/wcmc/2018/7095763/},
	author = {El Barrak, Soumaya and El Gonnouni, Amina and Serrano, Salvatore and Puliafito, Antonio and Lyhyaoui, Abdelouahid}
}
@conference { 11570_3115060,
	title = {Application of MVDR and MUSIC spectrum sensing techniques with implementation of Node{\textquoteright}s prototype for cognitive radio Ad Hoc networks},
	booktitle = {ACM International Conference Proceeding Series},
	volume = {130526},
	year = {2017},
	publisher = {Association for Computing Machinery},
	organization = {Association for Computing Machinery},
	address = {New York, NY 10121-0701},
	keywords = {1707, AdHoc Networks, Cognitive Radio, Computer Networks and Communications, GNU-Radio, Human-Computer Interaction, IoT, MUSIC, MVDR, Software, Spectrum Sensing, USRPb200},
	isbn = {9781450352819} pages = {101{\textendash}106},
	doi = {10.1145/3128128.3128144},
	url = {http://portal.acm.org/},
	author = {El Barrak, Soumaya and Lyhyaoui, Abdelouahid and El Gonnouni, Amina and Puliafito, Antonio and Serrano, Salvatore}
}
@conference { 11570_3115062,
	title = {SVM-MUSIC algorithm for spectrum sensing in cognitive radio ad-hoc networks},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {10517},
	year = {2017},
	publisher = {Springer Verlag},
	organization = {Springer Verlag},
	address = {Cham},
	abstract = {Adopting accurate and efficient spectrum sensing policy is crucial in allowing cognitive radio users to be aware of the surrounding parameters related to the radio environment characteristics. Especially, in Ad hoc networks scenario, where dynamic spectrum access is highly required, since the most of the spectrum is already assigned statistically, and the unlicensed bands are becoming overcrowded. This is due to the multiplicity of wireless communication technologies that operate in those bands, and the increasing number of connected devices. In this paper, a spectrum sensing algorithm that combines the Support Vector Machines (SVM) supervised learning technique with the Multiple Signal Characterization (MUSIC) subspace method is used. Our ultimate objective is detecting the presence of primary users (technology signals) in the band of interest. The node{\textquoteright}s receivers which make up the network collect samples from the radio environment, estimate the number of primary user signals and the corresponding carrier frequencies. Simulations are conducted to demonstrate the efficiency of the proposed SVM based algorithm in detecting the presence of primary users based on lost-detection and false alarm probabilities evaluation.},
	keywords = {Cognitive radio ad hoc networks (CRAHNs), Computer Science (all), Minimum variance distortionless response (MVDR), Multiple signal characterization (MUSIC), Spectrum sensing (SS), Support vector machines (SVM), Theoretical Computer Science},
	isbn = {9783319679099} pages = {161{\textendash}170},
	doi = {10.1007/978-3-319-67910-5_13},
	url = {https://link.springer.com/chapter/10.1007\%2F978-3-319-67910-5_13},
	author = {El Barrak, Soumaya and Lyhyaoui, Abdelouahid and Gonnouni, Amina El and Puliafito, Antonio and Serrano, Salvatore}
}
