Precise technology

OF SOUND DECOMPOSITION

Our technology is a new form of sound decomposition. To its advantages belongs a possibility to depict an audio signal precisely sample by sample in such a way than enables free manipulation of its all internal components. Sound Objects describing the signal inform about the amplitude, frequency and phase in every moment of time. Such signal decomposition allows for its accurate representation, analysis as well as separation and synthesis of its internal components, even incase of signals overlapping in frequency and time.

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New possibilities

OF AUTOMATIC SOUND SOURCES SEPARATION

The sound-into-sound-objects decomposition algorithms allow to separate automatically co-sounds whose frequencies differ only by 4% (around one semi-tone). Such precision of sound components separation allows for previously unattainable ability to recognize logical components of all sound sources as well as precision of their isolation.

Such precision of sound components separation together with preserved information about frequency, amplitude, position and phase of each Sound Object allows for previously unattainable ability to recognize logical components of all sound sources as well as precision of their isolation.

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Potential Applications

OF SOUND SEPARATION

Extraordinary quality

IN SOUND SIGNAL ANALYSIS

With specially designed algorithms, we are able to decompose an acoustic signal into vectorial Sound Objects and begin to group them according to their source.

Precise Sound Separation

BASED ON SOUND OBJECTS

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FILTERS

Unlike the traditional methods of sound processing such as fast Fourier transform (FFT) and its derivatives (DCT,  CQT), our technology engages uniquely designed bank of filters in order to obtain vectorial Sound Objects carrying information about precise parameters depicting the signal.

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SPECTRUM

The spectrogram achieved in the process of signal filtration, allows for precise separation of overlapping-in-time frequencies, which differ barely >4% (1 semitone). Moreover, it can be characterized by a unchangeable resolution in all 500 logarithmic frequency ranges.

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VECTORS

By the use of our algorithms we are able to identify  Sound Objects and begin to group them according to their source.