Finally we found some time to collect a few listening examples for our audio algorithms – thanks to all the people who provided audio files!
Everything is processed automatically and you can
try Auphonic yourself
with the unprocessed files and will get the same results.
The official Auphonic Audio Example page is here:
Audio Examples.
We will extend this page with further examples, so let us know if you have some great ones!
Global Loudness Normalization
Our Global Loudness Normalization Algorithms calculate the loudness of
your audio and apply a constant gain to reach a defined
target level in LUFS, so that all your processed files have
the same average loudness.
The loudness is calculated according to latest broadcast standards
and Auphonic supports loudness targets for television
(EBU R128, ATSC A/85), radio
and mobile.
See
Audio Loudness Measurement and Normalization and
Loudness Targets for Mobile Audio, Podcasts, Radio and TV
for detailed information.
The following example is an unprocessed studio recording from
Undsoversity
and demonstrates various loudness targets from quiet to loud.
2. Television US (ATSC A/85), -24 LUFS (no gate):
3. Television Europe (EBU R128), -23 LUFS:
4. Similar to ReplayGain, -18 LUFS:
5. Mobile Audio (similar to Sound Check), -16 LUFS:
Adaptive Leveler
The Auphonic Adaptive Leveler corrects level differences between speakers,
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 in one file.
For more details see
Loudness Normalization and Compression
.
- Quiet speakers are amplified in speech segments to achieve equal levels between speakers.
- Music segments are processed with care: the overall loudness will be comparable to speech, but we won't change the natural dynamics of your music as much as in speech segments.
- Background segments (noise, wind, breathing, etc.) won't be amplified as much as speech or music.
- Compressors and limiters are applied automatically to get a balanced, final mix.
The following example is a recording with the internal microphone of a mobile phone (Samsung Google Galaxy Nexus). The Adaptive Leveler will apply dynamic range compression, will amplify quiet speech and balance the volume between music and speech. Furthermore our Noise Reduction Algorithms will remove all broadband background noise.
1. Unprocessed Audio:
2. Processed with Adaptive Leveler (no Noise Reduction):
3. Processed with Adaptive Leveler and 100dB Noise Reduction:
Noise and Hiss Reduction
Our Noise Reduction Algorithms remove broadband background noise and hiss
in audio files with slowly varying backgrounds.
First the audio file is segmented in regions with different background
noise characteristics, then a noise print is extracted in each region
and removed from the audio signal.
For optimal performance, leave the noise as natural and constant as it is
and do not use noise gates, excessive dynamic range compression or
automatic gain control (for example in skype or on camcorders)!
The first Noise Reduction audio example is a recording by James Schramko with broadband background noise. Listen with headphones to hear all details!
Example 1, Unprocessed Audio:
Example 1, Processed with Noise and Hum Reduction (100dB Reduction Amount):
The second example is a short excerpt from the Newz of the World podcast with fans and brodband noise in the background.
Example 2, Unprocessed Audio:
Example 2, Processed with Noise and Hum Reduction (100dB Reduction Amount):
Hum Reduction
The Auphonic Hum Reduction Algorithms (included in
Noise and Hum Reduction)
will identify power line hum and all its partials.
Afterwards the partials are removed as necessary with sharp filters and
broadband noise reduction.
The following audio example by
FMC
contains a 60Hz power line hum with many partials (120Hz,
180Hz, 240Hz, 300Hz, etc.).
2. Processed with Noise and Hum Reduction (100dB Reduction Amount):
Try our Algorithms with your own Audio Files!