Search results
Results from the WOW.Com Content Network
A large family of signal processing techniques consist of Fourier-transforming a signal, manipulating the Fourier-transformed data in a simple way, and reversing the transformation. [10] Some examples include: Equalization of audio recordings with a series of bandpass filters;
Fractional wavelet transform (FRWT) is a generalization of the classical wavelet transform in the fractional Fourier transform domains. This transform is capable of providing the time- and fractional-domain information simultaneously and representing signals in the time-fractional-frequency plane. [30]
Wavelet compression is a form of data compression well suited for image compression (sometimes also video compression and audio compression).Notable implementations are JPEG 2000, DjVu and ECW for still images, JPEG XS, CineForm, and the BBC's Dirac.
The Fourier transform is one of the most common approaches when it comes to digital signal processing and signal analysis.It represents a signal through sine and cosine functions thus [further explanation needed] transforming the time-domain into frequency-domain.
Linear techniques such as Short-time Fourier transform, wavelet transform, filter bank, [11] non-linear (e.g., Wigner–Ville transform [10]) and autoregressive methods (e.g. segmented Prony method) [10] [12] [13] are used for representation of signal on the time-frequency plane. Non-linear and segmented Prony methods can provide higher ...
High-end commercial audio processing packages either combine the two techniques (for example by separating the signal into sinusoid and transient waveforms), or use other techniques based on the wavelet transform, or artificial neural network processing [citation needed], producing the highest-quality time stretching.
This permits signal processing using digital circuits such as digital signal processors, microprocessors and general-purpose computers. Most modern audio systems use a digital approach as the techniques of digital signal processing are much more powerful and efficient than analog domain signal processing. [11]
In mathematics, the Fourier transform (FT) is an integral transform that takes a function as input and outputs another function that describes the extent to which various frequencies are present in the original function. The output of the transform is a complex-valued function of frequency.