Removes unwanted noises from audio improving speech intelligibility.
Background Noise Removal (BNR) is an audio background noise removal model from NVIDIA. BNR removes unwanted noises from audio, which improves speech intelligibility and also improves the speech recognition accuracy of various ASR systems under noisy environments.
This model is ready for commercial use.
Use of this model is governed by the NVIDIA Open Model License.
Global
Architecture Type: Residual Convolutional Recurrent Neural Network (CRNN)
Network Architecture: SEASR
Number of model parameters: ~60M
Input Type(s): Audio
Input Format(s): PCM F32
Input Parameters: One-Dimensional (1D)
Other Properties Related to Input: Pulse Code Modulation (PCM) audio samples
with no encoding or pre-processing; 16kHz or 48kHz sampling rate required.
Output Type(s): Audio
Output Format: PCM F32
Output Parameters: One-Dimensional (1D)
Other Properties Related to Output: PCM audio samples at input sampling rate
with no encoding or post-processing.
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
Runtime Engine(s):
Supported Hardware Microarchitecture Compatibility:
Preferred/Supported Operating System(s):
The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
This AI model can be embedded as an Application Programming Interface (API) call into the software environment described above.
BNR v2.1
Data Modality:
Audio Training Data Size:
NVIDIA models are trained on a diverse set of public and proprietary datasets. The BNR 2.0 model is trained on a wide range of English language accents, some European and Asian languages, and 29 different noise profiles that are commonly audible in the real world.
Data Collection Method by dataset: [Hybrid: Human, Synthetic]
Labeling Method by dataset: [Hybrid: Human, Synthetic]
Link: AudioSet
Properties (Quantity, Dataset Descriptions, Sensor(s)):
AudioSet consists of an expanding ontology of 632 audio event classes and
a collection of 2,084,320 human-labeled 10-second sound clips drawn from
publicly available internet scale data.
Link: CREMA-D
Properties (Quantity, Dataset Descriptions, Sensor(s)):
CREMA-D is a data set of 7,442 original clips from 91 actors. These clips were
from 48 male and 43 female actors between the ages of 20 and 74 coming from a
variety of races and ethnicities (African American, Asian, Caucasian, Hispanic,
and Unspecified). Actors spoke from a selection of 12 sentences. The sentences
were presented using one of six different emotions (Anger, Disgust, Fear, Happy,
Neutral, and Sad) and four different emotion levels (Low, Medium, High, and
Unspecified).
Link: Crowdsourced high-quality UK and Ireland English Dialect speech data set
Properties (Quantity, Dataset Descriptions, Sensor(s)):
The dataset contains male and female high quality recordings of English from
various dialects of the UK and Ireland for a total of 17,877 lines.
Link: CSR-I WSJ0
Properties (Quantity, Dataset Descriptions, Sensor(s)):
Corpus by the DARPA Spoken Language Program to support research on large-
vocabulary Continuous Speech Recognition (CSR) systems. The first two CSR
Corpora consist primarily of read speech with texts drawn from a machine-
readable corpus of Wall Street Journal news text and are thus often known as
WSJ0 and WSJ1. WSJ0 consists of 123 speakers.
Link: CSTR VCTK
Properties (Quantity, Dataset Descriptions, Sensor(s)):
This CSTR VCTK Corpus includes speech data uttered by 110 English speakers with
various accents. Each speaker reads out about 400 sentences, which were selected
from a newspaper, the rainbow passage and an elicitation paragraph used for the
speech accent archive.
Link: DAPS
Properties (Quantity, Dataset Descriptions, Sensor(s)):
The DAPS dataset has 15 versions of audio (3 professional versions and
12 consumer device/real-world environment combinations). Each version consists
of about 4.5 hours of data (about 14 minutes from each of 20 speakers).
Link: DEMAND
Properties (Quantity, Dataset Descriptions, Sensor(s)):
The DEMAND (Diverse Environments Multichannel Acoustic Noise Database)
contains a set of recordings that allow testing of algorithms using
real-world noise in a variety of settings. This version provides 15 recordings.
All recordings are made with a 16-channel array, with the smallest distance
between microphones being 5 cm and the largest being 21.8 cm. It is a collection
of multichannel recordings of accoustic noise in diverse environments.
Link: Edinburgh 56 speaker dataset
Properties (Quantity, Dataset Descriptions, Sensor(s)):
Clean and noisy parallel speech database from 56 speakers designed to train and
test speech enhancement methods that operate at 48kHz.
Link: FreeField
Properties (Quantity, Dataset Descriptions, Sensor(s)):
A dataset of standardised 7690 10-second excerpts from Freesound field recordings.
Link: Freesound
Properties (Quantity, Dataset Descriptions, Sensor(s)):
Freesound is a collaborative collection of 620,291 free sounds which contains
speakers speaking in different emotions as well as female speakers speaking in
high pitched voices. The audio data also contains few noise profiles too.
Link: GTC Dataset
Properties (Quantity, Dataset Descriptions, Sensor(s)):
A collection of talks from NVIDIA GTC Conferences with a total of 103 speakers.
Link: HiFi-TTS
Properties (Quantity, Dataset Descriptions, Sensor(s)):
A multi-speaker English dataset for training text-to-speech models.
The HiFi-TTS dataset contains about 291.6 hours of speech from 10 speakers
with at least 17 hours per speaker sampled at 44.1 kHz.
Link: LibriTTS
Properties (Quantity, Dataset Descriptions, Sensor(s)):
LibriTTS is a multi-speaker English corpus of approximately 585 hours of read
English speech by 200 speakers, which is resampled at 16kHZ.
Link: Vocal set dataset
Properties (Quantity, Dataset Descriptions, Sensor(s)):
VocalSet is a singing voice dataset consisting of 10.1 hours of monophonic
recorded audio of professional singers demonstrating both standard and extended
vocal techniques on all 5 vowels. Existing singing voice datasets aim to capture
a focused subset of singing voice characteristics, and generally consist of just
a few singers. VocalSet contains recordings from 20 different singers (9 male,
11 female) and a range of voice types. VocalSet aims to improve the state of
existing singing voice datasets and singing voice research by capturing not only
a range of vowels, but also a diverse set of voices on many different vocal
techniques, sung in contexts of scales, arpeggios, long tones, and excerpts.
Acceleration Engine: Tensor(RT), Triton
Test Hardware:
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