Future Technology

Detecting Speech and Music in Audio Content material | by Netflix Know-how Weblog | Nov, 2023

7 min read

Iroro Orife, Chih-Wei Wu and Yun-Ning (Amy) Hung

Once you benefit from the newest season of Stranger Issues or Casa de Papel (Cash Heist), have you ever ever questioned concerning the secrets and techniques to incredible story-telling, apart from the beautiful visible presentation? From the violin melody accompanying a pivotal scene to the hovering orchestral association and thunderous sound-effects propelling an edge-of-your-seat motion sequence, the varied parts of the audio soundtrack mix to evoke the very essence of story-telling. To uncover the magic of audio soundtracks and additional enhance the sonic expertise, we want a strategy to systematically study the interplay of those parts, sometimes categorized as dialogue, music and effects.

On this weblog submit, we’ll introduce speech and music detection as an enabling expertise for a wide range of audio purposes in Movie & TV, in addition to introduce our speech and music exercise detection (SMAD) system which we lately printed as a journal article in EURASIP Journal on Audio, Speech, and Music Processing.

Like semantic segmentation for audio, SMAD individually tracks the quantity of speech and music in every body in an audio file and is helpful in content material understanding duties throughout the audio manufacturing and supply lifecycle. The detailed temporal metadata SMAD gives about speech and music areas in a polyphonic audio combination are a primary step for structural audio segmentation, indexing and pre-processing audio for the next downstream duties. Let’s take a look at just a few purposes.

Audio dataset preparation

Speech & music exercise is a vital preprocessing step to arrange corpora for coaching. SMAD classifies & segments long-form audio to be used in massive corpora, similar to

From “Audio Sign Classification” by David Gerhard

Dialogue evaluation & processing

  • Throughout encoding at Netflix, speech-gated loudness is computed for each audio grasp observe and used for loudness normalization. Speech-activity metadata is thus a central a part of correct catalog-wide loudness administration and improved audio quantity expertise for Netflix members.
  • Equally, algorithms for dialogue intelligibility, spoken-language-identification and speech-transcription are solely utilized to audio areas the place there’s measured speech.

Music info retrieval

  • There are just a few studio use circumstances the place music exercise metadata is vital, together with quality-control (QC) and at-scale multimedia content material evaluation and tagging.
  • There are additionally inter-domain duties like singer-identification and tune lyrics transcription, which don’t match neatly into both speech or classical MIR duties, however are helpful for annotating musical passages with lyrics in closed captions and subtitles.
  • Conversely, the place neither speech nor music exercise is current, such audio areas are estimated to have content material labeled as noisy, environmental or sound-effects.

Localization & Dubbing

Lastly, there are post-production duties, which benefit from correct speech segmentation on the the spoken utterance or sentence degree, forward of translation and dub-script technology. Likewise, authoring accessibility-features like Audio Description (AD) entails music and speech segmentation. The AD narration is usually mixed-in to not overlap with the first dialogue, whereas music lyrics strongly tied to the plot of the story, are generally referenced by AD creators, particularly for translated AD.

A voice actor within the studio

Though the appliance of deep studying strategies has improved audio classification programs lately, this information pushed method for SMAD requires massive quantities of audio supply materials with audio-frame degree speech and music exercise labels. The gathering of such fine-resolution labels is expensive and labor intensive and audio content material usually can’t be publicly shared as a result of copyright limitations. We deal with the problem from a unique angle.

Content material, style and languages

As a substitute of augmenting or synthesizing coaching information, we pattern the massive scale information obtainable within the Netflix catalog with noisy labels. In distinction to scrub labels, which point out exact begin and finish occasions for every speech/music area, noisy labels solely present approximate timing, which can impression SMAD classification efficiency. However, noisy labels enable us to extend the dimensions of the dataset with minimal handbook efforts and doubtlessly generalize higher throughout various kinds of content material.

Our dataset, which we launched as TVSM (TV Speech and Music) in our publication, has a complete variety of 1608 hours of professionally recorded and produced audio. TVSM is considerably bigger than different SMAD datasets and accommodates each speech and music labels on the body degree. TVSM additionally accommodates overlapping music and speech labels, and each lessons have an analogous whole length.

Coaching examples have been produced between 2016 and 2019, in 13 nations, with 60% of the titles originating within the USA. Content material length ranged from 10 minutes to over 1 hour, throughout the varied genres listed beneath.

The dataset accommodates audio tracks in three totally different languages, specifically English, Spanish, and Japanese. The language distribution is proven within the determine beneath. The identify of the episode/TV present for every pattern stays unpublished. Nonetheless, every pattern has each a show-ID and a season-ID to assist establish the connection between the samples. As an example, two samples from totally different seasons of the identical present would share the identical present ID and have totally different season IDs.

What constitutes music or speech?

To judge and benchmark our dataset, we manually labeled 20 audio tracks from varied TV exhibits which don’t overlap with our coaching information. One of many basic points encountered throughout the annotation of our manually-labeled TVSM-test set, was the definition of music and speech. The heavy utilization of ambient sounds and sound results blurs the boundaries between energetic music areas and non-music. Equally, switches between conversational speech and singing voices in sure TV genres obscure the place speech begins and music stops. Moreover, should these two lessons be mutually unique? To make sure label high quality, consistency, and to keep away from ambiguity, we converged on the next tips for differentiating music and speech:

  • Any music that’s perceivable by the annotator at a cushty playback quantity ought to be annotated.
  • Since sung lyrics are sometimes included in closed-captions or subtitles, human singing voices ought to all be annotated as each speech and music.
  • Ambient sound or sound results with out obvious melodic contours shouldn’t be annotated as music. Conventional telephone bell, ringing, or buzzing with out obvious melodic contours shouldn’t be annotated as music.
  • Stuffed pauses (uh, um, ah, er), backchannels (mhm, uh-huh), sighing, and screaming shouldn’t be annotated as speech.

Audio format and preprocessing

All audio information have been initially delivered from the post-production studios in the usual 5.1 encompass format at 48 kHz sampling charge. We first normalize all information to a median loudness of −27 LKFS ± 2 LU dialog-gated, then downsample to 16 kHz earlier than creating an ITU downmix.

Mannequin Structure

Our modeling decisions benefit from each convolutional and recurrent architectures, that are recognized to work effectively on audio sequence classification duties, and are effectively supported by earlier investigations. We tailored the SOTA convolutional recurrent neural community (CRNN) structure to accommodate our necessities for enter/output dimensionality and mannequin complexity. The perfect mannequin was a CRNN with three convolutional layers, adopted by two bi-directional recurrent layers and one totally linked layer. The mannequin has 832k trainable parameters and emits frame-level predictions for each speech and music with a temporal decision of 5 frames per second.

For coaching, we leveraged our massive and numerous catalog dataset with noisy labels, launched above. Making use of a random sampling technique, every coaching pattern is a 20 second phase obtained by randomly deciding on an audio file and corresponding beginning timecode offset on the fly. All fashions in our experiments have been skilled by minimizing binary cross-entropy (BCE) loss.


So as to perceive the affect of various variables in our experimental setup, e.g. mannequin structure, coaching information or enter illustration variants like log-Mel Spectrogram versus per-channel power normalization (PCEN), we setup an in depth ablation research, which we encourage the reader to discover totally in our EURASIP journal article.

For every experiment, we reported the class-wise F-score and error charge with a phase measurement of 10ms. The error charge is the summation of deletion charge (false destructive) and insertion charge (false constructive). Since a binary resolution should be attained for music and speech to calculate the F-score, a threshold of 0.5 was used to quantize the continual output of speech and music exercise capabilities.


We evaluated our fashions on 4 open datasets comprising audio information from TV applications, YouTube clips and varied content material similar to live performance, radio broadcasts, and low-fidelity folks music. The superb efficiency of our fashions demonstrates the significance of constructing a strong system that detects overlapping speech and music and helps our assumption that a big however noisy-labeled real-world dataset can function a viable resolution for SMAD.

At Netflix, duties all through the content material manufacturing and supply lifecycle work are most frequently interested by one a part of the soundtrack. Duties that function on simply dialogue, music or results are carried out a whole lot of occasions a day, by groups across the globe, in dozens of various audio languages. So investments in algorithmically-assisted instruments for automated audio content material understanding like SMAD, can yield substantial productiveness returns at scale whereas minimizing tedium.

We now have made audio options and labels obtainable by way of Zenodo. There’s additionally GitHub repository with the next audio instruments:

  • Python code for information pre-processing, together with scripts for five.1 downmixing, Mel spectrogram technology, MFCCs technology, VGGish options technology, and the PCEN implementation.
  • Python code for reproducing all experiments, together with scripts of information loaders, mannequin implementations, coaching and analysis pipelines.
  • Pre-trained fashions for every carried out experiment.
  • Prediction outputs for all audio within the analysis datasets.

Particular due to the complete Audio Algorithms group, in addition to Amir Ziai, Anna Pulido, and Angie Pollema.

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