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Modified Discrete Cosine Transform (MDCT) is a upcoming special transform implemented in areas of audio signal processing and compression. As, the name implies it is the modified form of DCT (Discrete Cosine Transform) that allows overlapping of segments (say 50%) and thereby it helps in avoiding artifacts. MDCT in general is a lapped transform that rectifies the problem of TDAC (Time Domain Aliasing Cancellation) and hence widely used in audio codec (.mp3, .wav) applications. The scope of this paper is to use MDCT in a system that overcomes the problem of TDAC and implementation of different windows those are capable with this transform. Finally, we calculate the MSE (Mean Squared Error) for the system with different inputs stating that perfect audio reconstruction is possible.

The MDCT and IntMDCT Algorithm is widely utilized is Audio coding. By lifting scheme or rounding operation IntegerMDCT is evolved from Modified Discrete Cosine Transform. This method acquire the properties of MDCT and contribute excelling invertiblity and good spectral mean .In this paper we discuss about the audio codec like AAC and FLAC using MDCT and Integer MDCT algorithm and to find which algorithm shows better Compression Ratio(CR).The confines of this task is to hybriding lossy and lossless audio codec with diminished bit rate but with finer sound quality. Certainly the quality of the audio is figure out by Subjective and Objective testing which is in terms of MOS (Mean opinion square), ABx and some of the hearing aid testing methodology like PEAQ(Perceptual Evaluation Audio Quality) and ODG(Objective Difference Grade)is followed. Execution measure, that is Compression Ratio(CR) and Sound Pressure Level (SPL) is approximated.

Transportation plays a major role of a person’s life. A life without transportation is unimaginable. People focus on four major types of transportation i.e. Roadways, Railways, Airways and Waterways. Among these transportation majority of people prefer railways as it helps connect distant locations and the comfort it provides. But the problem in railways is it needs more of monitoring and thereby the person travelling across areas can be at ease. In order to ensure safety in railway transportation, monitoring of tracks is very crucial. Hence, this research article focuses on monitoring the tracks present in railways through a novel image processing technique devised. The name of the technique we devised is known as CRACK TRACKER. Crack Tracker does the role of monitoring the tracks by capturing the images of the tracks and it is sent to the nearby station so that the cracks can be patched in the future. The cracks are clearly detected and intimated instantly to the station master. A reliable solution for continuous and cost efficient tracking is processed and depicted in this research article.

Audio Signals are the portrayal of sounds. It changes with respect to frequencies rather than time, and it shows more information in the frequency domain. So it is much appropriate to evaluate in the frequency domain rather than the time domain. By using different transforms like DFT, DST, DCT, MDCT, Integer MDCT, the time domain audio signal can be converted into a frequency domain signal. The signal is reconstructed to analyze the features like mean square error, Signal to noise ratio, Peak signal to noise ratio between the original and reconstructed signal. Other features like energy, entropy, zero crossing rates (ZCR) were also considered for the evaluation. In this paper, different audio file formats were taken for interpretation. It includes wave file, mp3 file, m4a file, aac file, where wave file is in uncompressed format and mp3, m4a, aac are in compressed format.  These compressed files come under lossy compression.  The above-mentioned features are used for applications like music information retrieval (MIR). MIR includes onset detection, pitch detection and to measure the noise and loudness of the music.