Audio and Music Classification and Separation

*Multimedia Information Retrieval*

2014;**1**(4):20-25

23;6(3):747-59.

[18] Martini B, Choo KK. Cloud forensic technical challenges and solutions: A snapshot. IEEE Cloud Computing.

[28] Yeo J. Using penetration testing to enhance your company's security. Computer Fraud & Security.

[29] Ficco M, Choraś M, Kozik R. Simulation platform for cybersecurity and vulnerability analysis of critical infrastructures. Journal of computational science. 2017

[30] Sabillon R, Serra-Ruiz J, Cavaller V. An effective cybersecurity training model to support an organizational awareness program: The Cybersecurity

(CATRAM). A Case Study in Canada. Journal of Cases on Information Technology (JCIT). 2019;21(3):26-39.

Awareness TRAining Model

2013;**2013**(4):17-20

Sep 1;**22**:179-186

[19] Liu A, Fu H, Li Y. Secure and Trustworthy Forensic Data Acquisition

Infrastructure. World Scientific Book

[20] Qiu S, Liu J, Shi Y, Li M, Wang W. Identity-based private matching over outsourced encrypted datasets. IEEE Transactions on cloud Computing. 2015;

Okolie SO. Vulnerabilities in network infrastructures and prevention/

containment measures. In: Proceedings of Informing Science & IT Education

[22] Broad J, Binder A. Hacking with kali, Practical Penetration Techniques.

[23] Baloch R. Ethical hacking and penetration testing guide. Vol. 29. CRC

[25] Singh S, Sharma PK, Moon SY, Moon D, Park JH. A comprehensive study on APT attacks and countermeasures for future networks and communications: challenges and solutions. The Journal of Supercomputing. 2019;**75**(8):4543-4574

[26] Northcutt S, Shenk J, Shackleford D,

Rosenberg T, Siles R, Mancini S. Penetration testing: Assessing your overall security before attackers do. Sponsored by Core Impact, SANS Analyst Program 2006;3(6):22.

[27] Bradbury D. Point to own: the problem with hacking tools. Computer Fraud & Security. 2011;**2011**(11):12-14

[24] Green J. Staying ahead of cyber-attacks. Network Security.

and Transmission in a Cloud

[21] Awodele O, Onuiri EE,

Conference (InSITE) 2012.

Waltham: Syngress; 2014

Press; 2017

2015;1;(2):13-6.

Chapters. 2018:167-191

**88**

**Chapter 6**

**Abstract**

will be introduced.

**1. Introduction**

**91**

frequency domain, time-frequency domain

*Abdullah I. Al-Shoshan*

Classification and Separation of

This chapter addresses the topic of classification and separation of audio and music signals. It is a very important and a challenging research area. The importance of classification process of a stream of sounds come up for the sake of building two different libraries: speech library and music library. However, the separation process is needed sometimes in a cocktail-party problem to separate speech from music and remove the undesired one. In this chapter, some existed algorithms for the classification process and the separation process are presented and discussed thoroughly. The classification algorithms will be divided into three categories. The first category includes most of the real time approaches. The second category includes most of the frequency domain approaches. However, the third category introduces some of the approaches in the time-frequency distribution. The approaches of time domain discussed in this chapter are the short-time energy (STE), the zero-crossing rate (ZCR), modified version of the ZCR and the STE with positive derivative, the neural networks, and the roll-off variance. The approaches of the frequency spectrum are specifically the roll-off of the spectrum, the spectral centroid and the variance of the spectral centroid, the spectral flux and the variance of the spectral flux, the cepstral residual, and the delta pitch. The time-frequency domain approaches have not been yet tested thoroughly in the process of classification and separation of audio and music signals. Therefore, the spectrogram and the evolutionary spectrum will be introduced and discussed. In addition, some algorithms for separation and segregation of music and audio signals, like the independent Component Analysis, the pitch cancelation and the artificial neural networks

**Keywords:** audio signal, music signal, classification, separation, time domain,

Audio signal processing is an important subfield of signal processing that is concerned with the electronic manipulation of audio signals [1–6]. The problem of discriminating music from audio has increasingly become very important as automatic audio signal recognition (ASR) systems and it has been increasingly applied in the domain of real-world multimedia [7]. Human's ear can easily distinguish audio without any influence of the mixed music [8–23]. Due to the new methods of the analysis and the synthesis processing of audio signals, the processing of musical signals has gained particular weight [16, 24], and therefore, the classical sound analysis methods may be used in the processing of musical signals [25–28]. Many

Audio and Music Signals
