Audio Post Production Algorithms (Singletrack)
The Auphonic Audio Post Production Algorithms analyze a master stereo/mono audio file and correct level differences between speakers, between music and speech and between multiple audio files to achieve a balanced overall loudness.
They include automatic Audio Restoration Algorithms, a True Peak Limiter and targets for common Loudness Standards (EBU R128, ATSC A/85, Podcasts, Mobile, etc.).
All algorithms were trained with data from our web service and they keep learning and adapting to new audio signals every day.
Audio examples for all algorithms with detailed annotations can be found at:
The Auphonic Adaptive Leveler corrects level differences between several speakers, or between music and speech and applies dynamic range compression to achieve a balanced overall loudness. In contrast to our Global Loudness Normalization Algorithms, which correct loudness differences between files, the Adaptive Leveler corrects loudness differences between segments within one file.
The algorithm was trained with over five years of audio files from our Auphonic Web Service and keeps learning and adapting to new data every day!
We analyze an audio signal to classify speech, music and background segments and process them individually by:
Amplifying quiet speakers in speech segments to achieve equal levels between speakers.
Carefully processing music segments so that the overall loudness will be comparable to speech, but without changing the natural dynamics of music as much as in speech segments.
Classifying unwanted segments (noise, wind, breathing, silence etc.) and then excluding them from being amplified.
Automatically applying compressors and limiters to get a balanced, final mix (see also Loudness Normalization and Compression).
Our Adaptive Leveler is most suitable for programs where dialog or speech is the most prominent content: podcasts, radio, broadcast, lecture and conference recordings, film and videos, screencasts etc.
For all details about parameter settings, please see: Audio Algorithms
Listen to Adaptive Leveler Audio Examples:
Our Loudness Normalization Algorithms calculate the loudness of your audio and apply a constant gain to reach a defined target level in LUFS (Loudness Units relative to Full Scale), so that multiple processed files have the same average loudness.
In combination with parameters of our Adaptive Leveler, you can define a set of parameters like integrated loudness target, maximum true peak level, MaxLRA, MaxM, MaxS, dialog normalization, ect., which are described in detail in Audio Algorithms.
The loudness is calculated according to the latest broadcast standards, so you never have to worry about admission criteria for different platforms or broadcasters again:
Auphonic supports loudness targets for television (EBU R128, ATSC A/85), radio and mobile (-16 LUFS: Apple Music, Google, AES Recommendation), Amazon Alexa, YouTube, Spotify, Tidal (-14 LUFS), Netflix (-27 LUFS), Audible / ACX audiobook specs (-20 LUFS) and more.
For more detailed information, please see our articles about: Audio Loudness Measurement and Normalization, Loudness Targets for Mobile Audio, Podcasts, Radio and TV, and The New Loudness Target War.
A True Peak Limiter, with 4x oversampling to avoid intersample peaks, is used to limit the final output signal and ensures compliance with the selected loudness standard.
We use a multi-pass loudness normalization strategy based on statistics from processed files on our servers, to more precisely match the target loudness and to avoid additional processing steps.
Listen to Loudness Normalization Audio Examples:
In the Auphonic Web Service, you can use three methods to define which kind of noise you want to reduce:
The Static Denoiser only removes stationary, technical noises, while our Dynamic Denoiser removes everything but voice and music. Select Speech Isolation if you only want to keep speech, so all noise and even music will be removed.
With Speech Isolation and Dynamic Denoiser also the reverb caused by rooms will be eliminated from your audio.
Warning: Please do not use the dynamic or speech isolation denoiser, if you want to keep sound effects or ambient sounds!
For more details about the available parameters please see Noise Reduction Settings.
Listen to Noise Reduction Audio Examples:
The Dynamic Denoiser preserves speech and music signals, but removes everything else from your audio file. It is perfectly used to remove fast-changing and complex noises, like reverberation from voice recordings in large rooms, environment sounds from outdoor recordings, background chatter from recordings in crowded places, and similar.
During the analysis of an audio signal, there is metadata generated, classifying contents such as spoken word, music in the foreground or background, different types of noises, silence, and more. Based on this metadata, the audio is processed with AI algorithms, that are permanently changing over time, to apply the best matching settings for every tiny segment of your audio. These adaptive noise reduction algorithms take care, that a consistent, high sound quality is produced throughout the entire recording, while removing as much unwanted noises as possible.
You can also manually set the parameter Noise Reduction Amount, if you prefer more noise reduction or want to quietly keep the ambient sounds, but increase the speech intelligibility. However, be aware that high noise reduction amounts might result in artifacts!
Please pay attention not to use the dynamic denoiser, if you want to keep sound effects or ambient sounds in any kind of audio play content!
Our Speech Isolatoion algorithms do only isolate speech, but remove everything else, including music, from your audio file. It is perfectly used with speech recordings containing music and fast-changing, complex noises. For example, videos with poor speech intelligibility, recodings in crowded places with music and chatter in the background, and similar situations.
During the analysis of an audio signal, there is metadata generated, classifying contents such as spoken word, music in the foreground or background, different types of noises, silence, and more. Based on this metadata, the audio is processed with AI algorithms, that are permanently changing over time, to apply the best matching settings for every tiny segment of your audio. These adaptive noise reduction algorithms take care, that a consistent, high sound quality is produced throughout the entire recording, while removing as much unwanted music and noises as possible.
You can also manually set the parameter Noise Reduction Amount, if you prefer more noise reduction or want to quietly keep music and ambient sounds, but increase the speech intelligibility. However, be aware that high noise reduction amounts might result in artifacts!
Please pay attention not to use speech isolation algorithms, if you want to keep jingles, sound effects or ambient sounds in any kind of audio play content!
Our Static Denoiser algorithms only remove broadband background noise and hum from audio files with slowly varying backgrounds. Therefore, static denoising is perfectly used for any kind of audio play content, where you want to keep sound effects or ambient sounds, like a singing bird in your ornithology podcast.
First the audio file is analyzed and segmented in regions with different background noise characteristics, and subsequently Noise Prints are extracted for each region.
Per default, there is also Hum Reduction activated with the Static Denoiser. If any hum is present in the recording, the hum base frequency (50Hz or 60Hz) and the strength of all its partials (100Hz, 150Hz, 200Hz, 250Hz, etc.) are also identified for each region.
Based on this metadata of all analyzed audio regions, our classifier finally decides how much noise and hum reduction is needed in each region and automatically removes the noise and hum from the audio signal.
You can also manually set the parameter Noise Reduction Amount, if you prefer more noise reduction. However, be aware that high noise reduction amounts might result in artifacts!
Keep the noise as natural and constant as it is, don’t try to improve or hide it yourself!
Please do not use leveling or gain control before our noise reduction algorithms! The amplification will be different all the time and we will not be able to extract constant noise prints anymore.
This means: no levelator, turn off automatic gain control in skype, audio recorders, camcorders and other devices …
No noise gates: we need the noise in quiet segments which noise gates try to remove!
Excessive use of dynamic range compression may be problematic, because noise prints in quiet segments get amplified.
Noise reduction might be problematic in recordings with lots of reverb, therefore try to keep the microphone close to your speakers!
Our adaptive High-Pass Filtering algorithm cuts disturbing low frequencies and interferences, depending on the context.
First we classify the lowest wanted signal in every audio segment: male/female speech base frequency, frequency range of music (e.g. lowest base frequency), noise, etc. Then all unnecessary low frequencies are removed adaptively in every audio segment, so that interferences are removed but the overall sound of the audio is preserved.
We use zero-phase (linear) filtering algorithms to avoid asymmetric waveforms: in asymmetric waveforms, the positive and negative amplitude values are disproportionate - please see Asymmetric Waveforms: Should You Be Concerned?.
Asymmetrical waveforms are quite natural and not necessarily a problem. They are particularly common on recordings of speech, vocals and can be caused by low-end filtering. However, they limit the amount of gain that can be safely applied without introducing distortion or clipping due to aggressive limiting.
The AutoEQ (Automatic Equalization) automatically analyzes and optimizes the frequency spectrum of a voice recording, to avoid speech that sounds very sharp, muddy, or otherwise unpleasant.
Using Auphonic AutoEQ, spectral EQ profiles are created for each speaker separately and permanently changing over time. The aim of those time-dependent EQ profiles is to create a constant, warm, and pleasant sound in the output file even if there are slightly changing voices in the record, for example, due to modified speaker-microphone positions.
For singletrack productions with more than one speaker, equalizing is also a very complex and time-consuming process, as every voice has its unique frequency spectrum and needs its own equalization. Without an AutoEQ feature one would have to separate speakers with cuts or create a track envelope to fade from one speaker to another tediously.
For more details, please also read our blog post Auphonic AutoEQ Filtering.
Listen to Filtering Audio Examples:
Our automatic silence cutting algorithm detects and removes silent segments, which occur in your audio recordings naturally. Silence can be due to short speech breaks, breathing pauses, or at the beginning, when the recording equipment is adjusted. Usually, listeners do not want to hear silence segments. Hence, cutting out silent segments is important to achieve a high-quality listening experience.
In the Auphonic Audio Inspector you can hide the cut regions by clicking the [?] (show legend) button and the Hide Silence Cutting Regions switch. When cut silent segments are hidden, also the timeline adjusts to cut length, so the timestamps of the speech recognition transcript match the audio inspector again.
For more details, please also read our blog post Automatic Silence Cutting.
IMPORTANT: Silence cutting is not available for video files!
Listen to Silence Cutting Audio Examples: