Summary
Musical tunes are bundle of chords representing emotion which impart diverse of genres. Past history highlighted copious amount of research work emotion and genre classification with still increasingly rapid advancement. Music has various emotional forms as happy, sad, anger and fear. Its various genre forms are Classical, Country, Disco, Hip‐hop, Jazz, and Rock. These emotions and genre can be segregated by identifying the frequency of chords notes (swarams in Tamil music). This paper deals with identifying emotions and genre for classical music both western and south Indian classical music, viz, Carnatic music. The music was clipped and segmented to determining frequency of notes using Shortest Fast Fourier Transformation (STFT). Music features such as mel frequency, pitch beat, zero crossing rate, and spectral centroid were derived from the obtained frequency. Based on the audio features, emotion and genre were identified for the given data set with genetic algorithm as a classification technique. The MIREX‐Mood classification dataset was considered for listing out emotions. The songs from Million song data set and emotion classification repository were considered as ground truth for western classical music and group of Illayaraja Tamil film songs was considered to identify Carnatic music emotions. The classification was done using genetic algorithm. Mel frequency, pitch, and zero crossing rate were considered as individual representations to get best fit ratio and it is found to give accuracy percentage of 99.03%.
Enhancement of speech signal degraded by several types of noise is a topic of interest for last many years. The main aim of speech enhancement algorithm is to improve the quality and/or intelligibility of the noisy speech signals by using various techniques and algorithms. Among the all available methods, the spectral subtraction algorithm is the one of the first algorithm proposed for removing additive background noise. This paper describes various noise reduction algorithms to analyse the performance of speaker verification in noisy environment. Speaker verification involves processing the speech signal and authenticating the speaker. The speaker verification process is affected by various factors such as noise, channel mismatch, health condition of the speaker, aging, emotion, fatigue etc. Speech signals from the uncontrolled environment may contain noise along with required speech components, speech signals are degraded by this noise and it renders the performance of speaker verification systems unacceptable. We enhance the speech signal by using the basic spectral subtraction and the modified approaches of spectral subtraction like spectral Subtraction with over subtraction, non-linear spectral subtraction and multiband spectral subtraction. The speech signal has been enhanced by each of the above methods then speaker models are trained with support vector machine using mel frequency cepstral coefficient of each speaker in the system. The performance of the speaker verification system is analysed with enhanced speech signal.
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