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FFT of audio, 2^8 samplingspacket sampling and others

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Hi I am just toying around with compression now that we are learning about fourierFourier transforms in my class, compression (compression is not a part of the class but I became a bit curious). Trying to do this with a wav file and I have some questions.

Assume I don't know the sampling rate of the signal (.wav) how can I figure out what the fftFFT frequency range is in Hz? I know that the bins are multiples of the samplingratesampling rate (Fs\$F_s\$) divided by N, or they are factors of Fs, eg 0.1*Fs1*\$F_s\$, 0.2*Fs2*\$F_s\$ etc... but how does this help me find the sampling frequency in Hz, so I can actually see which frequencies are doing what.?

Is there any clever trick in storing the coefficients of a real signal (wav)? They are always complex unless the signal is a pure cosine but what audio is? If I want to store the full range of coefficients I need at least as much storage as the .wav file (exploiting symmetry at the midpoint and having 2 units (real, imag) for every real input). This is of particular importance, if the coefficients are small they can be stored with less bits than the bits needed to represent the .wav datapoints.

Since for the case of functions you can split them in subintervalls and take the fourierFourier transform and depending on the result get some easier or more compact expression I was wondering if for the sake of compression I could split the audio-signal? 

If I have a .wav file with N datapoints then split into ten N/10 segments and then fft on each segment. But this then means that my sampling frequency is essentially reduced to N/10 Fs\$F_s\$? This seems intuitive, if I "zoom" into a rapidly osciallitaing function then in that window it will look very slow and appear to have a low frequency.

Thanks for any input!

Hi I am just toying around with compression now that we are learning about fourier transforms in my class, compression is not a part of the class but I became a bit curious. Trying to do this with a wav file and I have some questions.

Assume I don't know the sampling rate of the signal (.wav) how can I figure out what the fft frequency range is in Hz? I know that the bins are multiples of the samplingrate (Fs) divided by N, or they are factors of Fs, eg 0.1*Fs, 0.2*Fs etc but how does this help me find the sampling frequency in Hz so I can actually see which frequencies are doing what.

Is there any clever trick in storing the coefficients of a real signal (wav)? They are always complex unless the signal is a pure cosine but what audio is? If I want to store the full range of coefficients I need at least as much storage as the .wav file (exploiting symmetry at the midpoint and having 2 units (real, imag) for every real input). This is of particular importance, if the coefficients are small they can be stored with less bits than the bits needed to represent the .wav datapoints.

Since for the case of functions you can split them in subintervalls and take the fourier transform and depending on the result get some easier or more compact expression I was wondering if for the sake of compression I could split the audio-signal? If I have a .wav file with N datapoints then split into ten N/10 segments and then fft on each segment. But this then means that my sampling frequency is essentially reduced to N/10 Fs? This seems intuitive, if I "zoom" into a rapidly osciallitaing function then in that window it will look very slow and appear to have a low frequency.

Thanks for any input!

Hi I am just toying around with compression now that we are learning about Fourier transforms in my class (compression is not a part of the class but I became a bit curious). Trying to do this with a wav file and I have some questions.

Assume I don't know the sampling rate of the signal (.wav) how can I figure out what the FFT frequency range is in Hz? I know that the bins are multiples of the sampling rate (\$F_s\$) divided by N, or they are factors of Fs, eg 0.1*\$F_s\$, 0.2*\$F_s\$ etc... but how does this help me find the sampling frequency in Hz, so I can actually see which frequencies are doing what?

Is there any clever trick in storing the coefficients of a real signal (wav)? They are always complex unless the signal is a pure cosine but what audio is? If I want to store the full range of coefficients I need at least as much storage as the .wav file (exploiting symmetry at the midpoint and having 2 units (real, imag) for every real input). This is of particular importance, if the coefficients are small they can be stored with less bits than the bits needed to represent the .wav datapoints.

Since for the case of functions you can split them in subintervalls and take the Fourier transform and depending on the result get some easier or more compact expression I was wondering if for the sake of compression I could split the audio-signal? 

If I have a .wav file with N datapoints then split into ten N/10 segments and then fft on each segment. But this then means that my sampling frequency is essentially reduced to N/10 \$F_s\$? This seems intuitive, if I "zoom" into a rapidly osciallitaing function then in that window it will look very slow and appear to have a low frequency.

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Hi I am just toying around with compression now that we are learning about fourier transforms in my class, compression is not a part of the class but I became a bit curious. Trying to do this with a wav file and I have some questions.

Assume I don't know the sampling rate of the signal (.wav) how can I figure out what the fft frequency range is in Hz? I know that the bins are multiples of the samplingrate (Fs) divided by N, or they are factors of Fs, eg 0.1*Fs, 0.2*Fs etc but how does this help me find the sampling frequency in Hz so I can actually see which frequencies are doing what.

Is there any clever trick in storing the coefficients of a real signal (wav)? They are always complex unless the signal is a pure cosine but what audio is? If I want to store the full range of coefficients I need at least as much storage as the .wav file (exploiting symmetry at the midpoint and having 2 units (real, imag) for every real input). This is of particular importance, if the coefficients are small they can be stored with less bits than the bits needed to represent the .wav datapoints.

Since for the case of functions you can split them in subintervalls and take the fourier transform and depending on the result get some easier or more compact expression I was wondering if for the sake of compression I could split the audio-signal? If I have a .wav file with N datapoints then split into ten N/10 segments and then fft on each segment. But this then means that my sampling frequency is essentially reduced to N/10 Fs? This seems intuitive, if I "zoom" into a rapidly osciallitaing function then in that window it will look very slow and appear to have a low frequency.

Thanks for any input!

Hi I am just toying around with compression now that we are learning about fourier transforms in my class, compression is not a part of the class but I became a bit curious. Trying to do this with a wav file and I have some questions.

Assume I don't know the sampling rate of the signal (.wav) how can I figure out what the fft frequency range is in Hz? I know that the bins are multiples of the samplingrate (Fs) divided by N, or they are factors of Fs, eg 0.1*Fs, 0.2*Fs etc but how does this help me find the sampling frequency in Hz so I can actually see which frequencies are doing what.

Is there any clever trick in storing the coefficients of a real signal (wav)? They are always complex unless the signal is a pure cosine but what audio is? If I want to store the full range of coefficients I need at least as much storage as the .wav file (exploiting symmetry at the midpoint and having 2 units (real, imag) for every real input).

Since for the case of functions you can split them in subintervalls and take the fourier transform and depending on the result get some easier or more compact expression I was wondering if for the sake of compression I could split the audio-signal? If I have a .wav file with N datapoints then split into ten N/10 segments and then fft on each segment. But this then means that my sampling frequency is essentially reduced to N/10 Fs? This seems intuitive, if I "zoom" into a rapidly osciallitaing function then in that window it will look very slow and appear to have a low frequency.

Thanks for any input!

Hi I am just toying around with compression now that we are learning about fourier transforms in my class, compression is not a part of the class but I became a bit curious. Trying to do this with a wav file and I have some questions.

Assume I don't know the sampling rate of the signal (.wav) how can I figure out what the fft frequency range is in Hz? I know that the bins are multiples of the samplingrate (Fs) divided by N, or they are factors of Fs, eg 0.1*Fs, 0.2*Fs etc but how does this help me find the sampling frequency in Hz so I can actually see which frequencies are doing what.

Is there any clever trick in storing the coefficients of a real signal (wav)? They are always complex unless the signal is a pure cosine but what audio is? If I want to store the full range of coefficients I need at least as much storage as the .wav file (exploiting symmetry at the midpoint and having 2 units (real, imag) for every real input). This is of particular importance, if the coefficients are small they can be stored with less bits than the bits needed to represent the .wav datapoints.

Since for the case of functions you can split them in subintervalls and take the fourier transform and depending on the result get some easier or more compact expression I was wondering if for the sake of compression I could split the audio-signal? If I have a .wav file with N datapoints then split into ten N/10 segments and then fft on each segment. But this then means that my sampling frequency is essentially reduced to N/10 Fs? This seems intuitive, if I "zoom" into a rapidly osciallitaing function then in that window it will look very slow and appear to have a low frequency.

Thanks for any input!

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