1 Sachin M. Narangale, 2 Prof. Dr. G. N. Shinde. Int. Journal of Engineering Research and Application www.ijera.com ISSN : 2248-9622, Vol. 6, Issue 3, ( Part -4) March 2016, pp.171-174 www.ijera.com 171|Page Snorm–A Prototype for Increasing Audio File Stepwise Normalization 1 Sachin M. Narangale, 2 Prof. Dr. G. N. Shinde 1 School of Media Studies, Swami Ramanand Teerth Marathwada University, Nanded-431606, India 2 Pro-Vice Chancellor, Swami Ramanand Teerth Marathwada University, Nanded-431606, India ABSTRACT This paper introduces a novel concept SNORM (Step NORMalization) for increasing normalization. It is a prototype algorithm for increasing normalization based on loudness factor of the audio. The function effect normalization plays a vital role in loudness control. The proposed experiment carried out for increasing normalization based on step wise increase yield the variations in peaks of the audio file. The experimental results are shown in the form of graphical analysis of the plot spectrum values of frequency analysis. From the results, it is clearly apparent that, the normalization values are increased at different levels. The function SNORM can set a new benchmark in the field of audio industry for the processes of increasing normalization. SNORM can be substantial in the audio broadcast systems for applications in live audio streaming, news broadcast, sports coverage, live programming where the loudness control mechanism is essential. For the selective or predictive loudness control systems SNORM can be effectively applied. Keywords: Normalization, Loudness Control, Track normalization, Audacity, Audio Retrieval, Text to Speech application, Frequency Analysis I. INTRODUCTION The retrieval systems for audio are major game players in the music industry. The content- based audio information retrieval system (AIRS) for different electronic equipments is creating lot of attention. AIRS defined properly will definitely perform various retrieval operations including recognition, referencing and recommendation. Content-based music information retrieval (MIR) systems such as Shazam, SoundHound, and Gracenote have already been developed for the iPhone, iPad, and other similar Smartphone devices [1]. Audio retrieval is a mechanism that searches music that is being played. The audio identification and retrieval system has to identify the different background noises. For building AIRS, audio tokenization or unique identification is necessary. These unique identification measures act as the identity of the audio to be retrieved from the database. This audio unique identification measure contains brief details of the audio file or sometimes a frame of audio too. The growth of the audio industry has invariably led to the demand for quick and correct audio data retrieval. The audio retrieval system resulting lot of information none of the use is more. The audio retrieval rates need to be improved. To improve the retrieval rate, before the query is to be fired for the audio, first, it is essential to create a audio lookup reference table (ALURT). For audio to be matched the audio unique identification is maintained as the private key of retrieval. Due to this the audio retrieval probability of intended audio increases quantitatively. In this research, a mechanism in the form of prototype pre-processing method for retrieving the intended audio from ALURT is presented. The novel proposed mechanism SNORM works on the principle of normalization. A study has been evaluated for increasing normalization stepwise to counter the normalization values of the audio file. Normalization is a process of reducing peak amplitude of the audio signal to a defined intended level or to the corresponding average of the frame of an audio signal. Normalization is the process of modification of the amount of gain or amplitude of the audio signal. The peak amplitude is reduced to a target level in normalization. Audio Normalization is a pre-process of the system used in audio compression [2]. Another effective use of normalization can be seen in the process of loudness control. Normalization works out to smooth out the variations in the loudness. Peak control and loudness control can be achieved using normalization [3]. Normalization is an effect provided in the Audacity to guarantee the complete representation of every audio feature element. The process involves the methodology of subtracting the mean of audio feature and the resultant value is divided by its standard deviation [4]. Various application areas of the normalization process are: • Process of loudness control • Smooth out the variations in the loudness RESEARCH ARTICLE OPEN ACCESS