Download
audio representation and processing n.
Skip this Video
Loading SlideShow in 5 Seconds..
Audio Representation and Processing PowerPoint Presentation
Download Presentation
Audio Representation and Processing

Audio Representation and Processing

147 Vues Download Presentation
Télécharger la présentation

Audio Representation and Processing

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Audio Representation and Processing

    CIS 465 Multimedia
  2. Fundamentals of Audio Signals Two signals of different amplitudes A greater amplitude represents a louder sound.
  3. Fundamentals of Audio Signals Two signals of different frequencies A greater frequency represents a higher pitched sound.
  4. Fundamentals of Audio Signals Any sound, no matter how complex, can be represented by a waveform. For complex sounds, the waveform is built up by the superposition of less complex waveforms The component waveforms can be discovered by applying the Fourier Transform Converts the signal to the frequency domain Inverse Fourier Transform converts back to the time domain
  5. Sampling Sounds can be thought of as functions of a single variable (t) which must be sampled and quantized The sampling rate is given in terms of samples per second, or, kHz During the sampling process, an analog signal is sampled at discrete intervals At each interval, the signal is momentarily “held” and represents a measurable voltage rate
  6. Quantization Audio is usually quantized at between 8 and 20 bits Voice data is usually quantized at 8 bits Professional audio uses 16 bits Digital signal processors will often use a 24 or 32 bit structure internally
  7. Quantization The accuracy of the digital encoding can be approximated by considering the word length per sample This accuracy is known as the signal-to-error ratio (S/E) and is given by: S/E = 6n + 1.8 dB n is the number of bits per sample
  8. Quantization When a coarse quantization is used, it may be useful to add a high-frequency signal (analog white noise) to the signal before it is quantized This will make the coarse quantization less perceptible when the signal is played back This technique is known as dithering During the sampling process, an analog signal is sampled at discrete intervals At each interval, the signal is momentarily “held” and represents a measurable voltage rate
  9. Channels We may also have audio data coming from more than one channels Data from a multichannel source is usually interleaved Sampling rates are always measured per channel Stereo data recorded at 8000 samples/second will actually generate 16,000 samples every second
  10. Digital Audio Data A complete descriptionofdigital audio data includes (at least): sampling rate; numberofbits per sample; numberofchannels (1for mono, 2for stereo, etc.) Typeofquantization (linear, logarithmic, etc.)
  11. Analog to Digital Conversion Nyquist’s theorem states that if an arbitrary signal has been run through a low-pass filter of bandwidth H, the filtered signal can be completely reconstructed by taking only 2H (exact) samples per second. So, a low-pass filter is placed before the sampling circuitry of the analog-to-digital (A/D) converter.
  12. Analog to Digital Conversion If frequencies greater than the Nyquist limit enter the digitization process, an unwanted condition called aliasing occurs The low-pass filter used will require the use of a gradual high-frequency roll-off, thus a sampling rate somewhat higher than twice the Nyquist limit is often used A/D conversion may make use of a successive approximation register (SAR)
  13. Analog to Digital Conversion The low-pass filter can cause side effects. One way that these side effects can be overcome is through the use of oversampling - a signal-processing function that raises the sample rate of a digitally encoded signal. Consumer and professional 16-bit D/A converters often use up to 8- and 12-times oversampling, raising the sampling rate of a CD (for example) from 44.1 kHz to 352.8 kHz or 529.2 kHz. By altering the signal’s noise characteristics, it is possible to shift much of the overall bandwidth noise out of the range of human hearing.
  14. Pulse Code Modulation The method that has been discussed for storing audio is known as pulse code modulation (PCM).
  15. Pulse Code Modulation PCM is common in long-distance telephone lines. The analog signal (voice) is sampled at 8000 samples/second with 7 or 8 bits per sample A T1 carrier handles 24 voice channels multiplexed together The bandwidth of this type of carrier can be calculated as follows: 8 bits x 8000 samples/second x 24 channels = 1.544 Mbps Note that one out of 8 bits is for control, not data.
  16. Pulse Code Modulation D/A conversion process parallelize the serial bit stream generate an analog voltage analogous to the voltage level at the original time of sampling An output sample and hold circuit is used to minimize spurious signal glitches a final low-pass filter is inserted into the path Smooths out the non-linear steps introduced by digital sampling
  17. Pulse Code Modulation Other PCM topics: mu-law and A-law companding DPCM DM ADPCM
  18. Digital Signal Processing Processing of a digital signal to achieve special effects may generally be described in terms of some simple functions: Addition Multiplication Delay Resampling
  19. Digital Signal Processing Addition of two signals is accomplished by adding the sample values of the signals at each sampling point: h(t)=f(t)+g(t) We can add as many signals as desired together Multiplication of a given signal is represented as: g(t)=m*f(t), where m is the multiplication factor. Multiplication is used to increase or decrease the gain (loudness) of a signal. If m>1, g is louder than f. If m<1, g is less loud than f Note that when adding signals together or multiplying by a number greater than one, care must be taken when the signal reaches the upper limit of the sample size
  20. Digital Signal Processing Delay is an important effect described as follows: g(t)=f(t+d), where d is a delay time Use delay and addition to model echo: f(t) = HELLO g(t) = f(t + d1) , where 0 <d1 g(t) = HELLO h(t) = f(t + d2) , where 0 <d1 < d2 h(t) = HELLO F(t) = f(t) + g(t) + h(t) = HELLO HELLO HELLO
  21. Digital Signal Processing Now consider a more realistic echo effect. We need to make each succeeding echo softer. We can do this with multiplication. g’(t) = m*g(t)h’(t) = n*h(t), 0<n<m<1 F’(t) = f(t) + g’(t) + h’(t) = HELLO HELLOHELLO
  22. Digital Signal Processing When delays of 35-40 ms and greater are used, the listener perceives them as discrete delays Reducing the delay to the 15-35 ms range will create delays that are too closely spaced to be perceived as discrete delays When used with instruments, the brain is fooled into thinking that more instruments are playing than there actually are combining several short term delay modules that are slightly detuned in time, an effect known as chorusing can be achieved (used by guitarists, e.g.)
  23. Pitch-Related Effects DSP functions are available that can alter the speed and pitch of an audio program. These can: Change pitch without changing duration Change duration without changing pitch Change both duration and pitch The process for raising and lowering the pitch of a sample is shown on the next slides
  24. Pitch-Related Effects
  25. Pitch-Related Effects
  26. Noise Elimination The noise elimination process can be seen to consist of three steps: Visual analysis De-clicking De-noising Use visual analysis to determine the type of noise and to guide the next two steps
  27. Noise Elimination De-clicking involves the removal of noise generated by analog side effects such as tape hiss, needle ticks, pops, etc. This is similar to ‘snow’ removal in image processing (the noise manifests itself as large discontinuities in the sample waveform) The noise is likely to have affected more sample data in the audio file than in the corresponding image file A needle skip which affects 1/4 second of the file affects 11000 samples at the audio CD sampling rate Therefore, reconstruction of the affected area is not the straightforward linear interpolation process used in images Must examine a large portion of the waveform to reconstruct
  28. Noise Elimination De-noising involves the removal of background noise such as hum, buzzes, air-conditioner noises, etc The waveform is analyzed to determine if louder sounds will mask the softer sound This involves breaking down the audio spectrum into a large number of frequency bands The signal is compared with a signature which represents the background noise. This is taken from a silent moment in the samplefile. It must be determined which portion of a signal is noise and whether the noise can be deleted without distorting the program
  29. Digital Signal Processing Other DSP functions include digital mixing and sample rate conversion Digital mixing is the integration of a number of digital audio signals into a single ouput signal Sample rate conversion is necessary when a signal sampled at one rate must be played back on or transferred to equipment which uses another rate An example is the use of digital audio as the sound track for video. The incoming rate of 44.1 kHz must be “pulled-down” to 44.056 kHz
  30. Fading Fading is another important DSP function During a fade, the calculated sample amplitudes are either proportionately reduced or proportionately increased in level, according to a defined curve ramp For example, usually when performing a fade out, the signal will begin at a level that is 100 percent of its current value and will reduce over the defined time to 0 percent Examples of various fade curves are shown in the following slides
  31. Fading
  32. Fading
  33. Fading To find the linearly faded value of a sample at time tx, t0≤tx≤t1, we use the following equation: s’(tx) = s(tx) * (tx - t0) / (t1 - t0) We can also combine the fade in of one soundfile with the fade out of another soundfile to produce the effect known as crossfade
  34. Fading
  35. Fading Note that the two curves intersect at 50% attenuation and that the sum of the two values at any point in time is always 100% Thus, we can add together the two signals to form our crossfaded signal and the amplitude of the waveform will never be greater than the maximum possible amplitude