If you want to use our multitrack version, please also read our This crosstalk (spill), a reverb or echo-like effect, can be removed by theĬrossgate, because we know exactly when and in which trackĪuphonic Multitrack Algorithms Documentationįor detailed information about our multitrack algorithms. The voice of speaker 1 will also be recorded in the microphone of speaker 2. If one records multiple people with multiple microphones in one room, This results in much less noise in the final mixdown. The Adaptive Noise Gate decreases the volume of segments whereĪ speaker is inactive, but does not change segments where a speaker is active. The noise of all tracks will add up as well. If audio is recorded with multiple microphones and all signals are mixed, Noise profiles are extracted in individual tracks for automaticįurthermore we have added two multitrack-only audio algorithms: Produce a balanced loudness between tracks.ĭynamic range compression is applied to speech only – music segments are kept Knows exactly which speaker is active in which track and can therefore Using the knowledge from signals of all tracks allows us to produce much better results compared to our singletrack version: Speech tracks recorded from multiple microphones, music tracks, remote speakers via phone, skype, etc.Īuphonic processes all tracks individually as well as in combination and creates the final mixdown automatically. The Auphonic Multitrack Algorithms use multiple parallel input audio tracks in one production: Will amplify quiet speech and balance the volume between music and speech.įurthermore our Noise Reduction Algorithms The Adaptive Leveler will apply dynamic range compression, Mobile phone (Samsung Google Galaxy Nexus). The following example is a recording with the internal microphone of a Lecture and conference recordings, film and videos, screencasts etc. Where dialog or speech is the most prominent content: podcasts, radio, broadcast, Our Adaptive Leveler is most suitable for programs Segments and process them individually by:Īmplifying quiet speakers in speech segments to achieve equalĬarefully processing music segments so that the overall loudness will be comparable to speech,īut without changing the natural dynamics of music as much as in speech segments.Ĭlassifying unwanted segments (noise, wind, breathing, silence etc.) and then excluding them from being amplified.Īutomatically applying compressors and limiters to get a balanced, final mix We analyze an audio signal to classify speech, music and background The algorithm was trained with over three years of audio filesĪnd keeps learning and adapting to new data every day! Loudness differences between files, the Adaptive Leveler corrects loudness differences between segments in one file. In contrast to our Global Loudness Normalization The Auphonic Adaptive Leveler corrects level differences between speakers,īetween music and speech and applies dynamic range compression to
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