It is disclosed a method comprising obtaining a target spectrum, obtaining a set of non-target spectra, the set of non-target spectra comprising one or more non-target spectra, summing the target spectrum and the set of non-target spectra to obtain a mixture spectrum, and training an artificial neural network by using the mixture spectrum as input of the neural network and by using a spectrum which is based on the target spectrum as desired output of the artificial neural network.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising obtaining a target spectrum, obtaining a set of non-target spectra, the set of non-target spectra comprising one or more non-target spectra, summing the target spectrum and the set of non-target spectra to obtain a mixture spectrum, and training an artificial neural network by using the mixture spectrum as input of the neural network and by using a spectrum which is based on the target spectrum as desired output of the artificial neural network. 2. The method of claim 1 in which the target spectrum is obtained from an audio signal produced by a first instrument, and the set of non-target spectra comprises a non-target spectrum that is obtained from an audio signal produced by a second instrument. 3. The method of claim 1 further comprising using the target spectrum as desired output of the artificial neural network when training the artificial neural network. 4. The method of claim 1, further comprising determining a morphing spectrum based on the target spectrum and based on a replacement spectrum, using the morphing spectrum as desired output of the artificial neural network when training the artificial neural network. 5. The method of claim 4 in which the target spectrum is obtained from an audio signal produced by a first instrument, and in which the replacement spectrum is obtained from an audio signal produced by a replacement instrument which produces audio that corresponds to that of the first instrument. 6. The method of claim 1 in which the training of the artificial neural network is based on spectral amplitudes. 7. The method of claim 1 in which the obtaining a target spectrum, the obtaining a set of non-target spectra and the summing the target spectrum and the set of non-target spectra is repeated multiple times and in which the training of the artificial neural network is based on multiple mixture spectra and multiple target spectra. 8. Artificial neural network which has been trained according to the method of claim 1. 9. A system comprising circuitry, the circuitry implementing an artificial neural network which is trained to extract audio of a target instrument from an input audio signal containing audio of a mixture of instruments. 10. A system comprising circuitry, the circuitry implementing an artificial neural network which is trained to morph audio of a target instrument in an input audio signal containing audio of a mixture of instruments into corresponding audio of a replacement instrument. 11. A system comprising circuitry, the circuitry implementing an artificial neural network which is configured to receive an input spectrum, and to process the input spectrum to obtain an output spectrum, wherein the artificial neural network has been trained by using a mixture spectrum as input of the artificial neural network and by using a spectrum that is based on a target spectrum as desired output of the artificial neural network, the mixture spectrum corresponding to a sum of the target spectrum and a set of non-target spectra, the set of non-target spectra comprising one or more non-target spectra. 12. The system of claim 11 further comprising a time-to-frequency transformation unit, wherein the time-to-frequency transformation unit is configured to convert an input audio signal from a time domain to a frequency domain to obtain the input spectrum. 13. The system of claim 11 further comprising a frequency-to-time transformation unit, wherein the frequency-to-time transformation unit is configured to convert the output spectrum from a frequency domain to a time domain to obtain an output audio signal. 14. The system of claim 11 in which the artificial neural network processes spectral amplitudes. 15. The system of claim 11 further comprising a time-to-frequency transformation unit and a frequency-to-time transformation unit, wherein the time-to-frequency transformation unit is configured to convert an input audio signal from a time domain to a frequency domain to obtain spectral phases and spectral amplitudes, the artificial neural network is configured to receive the spectral amplitudes from the time-to-frequency transformation unit as input spectrum and to process the spectral amplitudes to obtain processed spectral amplitudes as output spectrum, the frequency-to-time transformation unit is configured to receive the spectral phases from the time-to-frequency transformation unit, to receive the processed spectral amplitudes from the artificial neural network, and to convert the spectral phases and the processed spectral amplitudes from a frequency domain to a time domain to obtain a processed audio signal. 16. The system of claim 11 in which the circuitry is further configured to damp the processed audio spectrum. 17. The system of claim 11 in which the circuitry is further configured to determine an output audio signal based on the input audio signal and the processed spectrum. 18. The system of claim 17 in which the circuitry is further configured to generate a target audio signal from the target spectrum, to weight the target audio signal by a damping factor, and to subtract the weighted target audio signal from the input audio signal to obtain the output audio signal. 19. The system of claim 18, used in a Karaoke machine. 20. A system comprising a database for storing parameters of neural network, a user interface for selecting at least a target instrument, and circuitry, the circuitry implementing an artificial neural network which is configured according to parameters retrieved from the database and selected via the user interface, the artificial neural network being further configured to process a mixture spectrum which corresponds to input music in order to obtain an output spectrum based on the parameters selected via the user interface.