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Le Sound LS Levelator

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$119.00
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Quick Overview

Le Sound LS Levelator

 

LS Levelator

LS Levelator is an energy/loudness normalization tool for batch processing of large amount of files. This tool not only makes use of local energy variation but also takes into account the loudness model to enable a perceptually relevant normalization. The batch process is expected to help localization and broadcast professionals to render the dialogue dynamics more efficiently and effectively.

 

New Features

Clean mouth clicks caused by watering mouth or unstable pronunciation
Dynamic range cmpression
Loudness and Segment displays for visual investigation
Compare and listen to original/processed waveforms
Supports custom presets

Main Features

Automatic loudness/energy normalization batch processing
Innovative global dynamic normalization and segment based processing
Preprocessing for cleaning mouth noise and dynamic range compression
Standalone application for macOS  and Windows

Processing Mode

Peak: This is a traditional normalization w.r.t. the maximum peak level.
Static: This mode normalizes a signal from the average loudness to the target loudness, with constrains defined by the peak level. When "Segment Based" processing is enabled, it allows to apply respective normalization to the automatically detected signal segments.
Dynamic: Similar to Static mode, Dynamic mode further allows adjusting the loudness dynamics among segments. When "Global Normalization" is enabled, all segments detected in all the input files are taken into account for deriving the best loudness distribution.
Bypass: Preprocessing of Clean and Compressor only without applying the normalization.

Processing Modules

Clean : Prevent normalization in the automatically detected noise region
Compressor : Dynamic range compression. Set the Comp. Ratio to 0 to use it as limiter.
Segment : Treat automatically detected segments respectively
Loudness : Loudness normalization 
Compare : Visual display and audio preview for easy comparison between the original and the processed file

 

Example

Original

 before_LSL

Processed

after_LSL

Variant1
SpecialsNov 8, 2019