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Music And AudioTop 10 Best Mic Background Noise Reduction Software of 2026
Ranking and tradeoffs for mic background noise reduction software. Covers Krisp, Adobe Podcast Enhance, Auphonic, Dolby On, OBS, NoiseTorch.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
Gitnux may earn a commission through links on this page — this does not influence rankings. Editorial policy
Dolby On is the best pick for live mic capture when you want consistent noise reduction without heavy post-work, whereas OBS Noise Suppression fits if OBS is your audio hub for streams and recordings and you want suppression right in the chain.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Dolby On
Speech-focused real-time capture tuning that prioritizes intelligibility over uniform ambience removal.
Built for fits when live mic audio needs consistent noise reduction without heavy post-workflow..
OBS Noise Suppression
Editor pickRNNoise inference as an OBS audio filter that applies inside the live capture pipeline.
Built for fits when OBS is the audio hub for live streaming or recordings..
NoiseTorch
Editor pickRNNoise-based, low-latency on-device denoising with live monitoring and quick bypass testing.
Built for fits when individual users need real-time mic denoising with local processing for calls and streaming..
Related reading
Comparison Table
Dolby On
mobileMobile recording app that applies noise reduction and voice enhancement to microphone captures.
Speech-focused real-time capture tuning that prioritizes intelligibility over uniform ambience removal.
Dolby On targets mic background noise by combining speech-focused filtering with adaptive suppression that reacts to changing room sounds. The product is aimed at workflows that need consistent denoising during live audio capture, not only offline post-processing. It also provides preset-style control so users can keep tuning changes aligned with typical speaking conditions.
A key tradeoff is that its speech-first approach can soften some non-speech details like keyboard click transients when they overlap with vocal timing. It fits best for remote meetings and live sessions where denoising latency overhead matters more than perfect offline restoration.
- +Speech-first denoising keeps intelligibility higher than generic noise gates
- +Low-latency capture mode supports real-time speaking turns
- +Adaptive suppression reacts to changing HVAC hum and room noise
- +Preset-style controls reduce re-tuning across common environments
- –Keyboard clicks and other transient sounds can get overly smoothed
- –Advanced parameter control is limited versus Auphonic batch pipelines
- –Room-specific tuning often takes more iterations than Krisp
- –Deep ecosystem integration is narrower than WebRTC-focused tools
Remote support teams
Reduce mixed office noise during calls
Fewer misunderstandings on calls
Live podcast production
Denoise room hum during recording
Cleaner live takes
Show 2 more scenarios
Classroom instructors
Maintain speech clarity in shared rooms
More intelligible lectures
Adaptive suppression targets consistent background sounds without requiring offline edits.
Streamers on desktop
Tighten mic audio for live chat
Less background distraction
Low-latency denoising reduces ambient noise so voice cuts through without delay.
Best for: Fits when live mic audio needs consistent noise reduction without heavy post-workflow.
More related reading
OBS Noise Suppression
creatorBuilt-in OBS Studio filter that reduces microphone background noise using Speex or RNNoise processing.
RNNoise inference as an OBS audio filter that applies inside the live capture pipeline.
OBS Noise Suppression is implemented as an OBS audio filter, so the denoise step sits alongside other OBS filters in the same processing graph. RNNoise inference provides a consistent noise-reduction effect without needing an ambient profile fingerprinting step. It is a fit when live streaming, recording, or remote broadcast setups already centralize audio in OBS and need mic denoising at capture time.
A practical tradeoff is that the denoiser lives inside OBS, so use outside OBS requires separate software or virtual audio routing. It works best when the mic issue is continuous background noise rather than abrupt events like keyboard clicks or short HVAC spikes that need per-event spectral gating tuning.
- +RNNoise denoising runs directly in OBS audio filter chains
- +Low-friction setup for mic noise reduction in live capture graphs
- +Scene-based OBS configuration keeps denoise tied to recordings or streams
- –Effect is scoped to OBS, so non-OBS apps need routing or substitutes
- –Limited control compared with tools that expose thresholding and preset tuning
Streamers running OBS scenes
Reduce constant room hiss during broadcasts
Cleaner voice in VOD audio
Remote interview producers
Denoise interview mics in recordings
Consistent denoised takes
Show 1 more scenario
Team meeting hosts
Lower steady keyboard and desk hum
Lower distractions for listeners
Use OBS denoise on capture to reduce continuous electrical or room noise.
Best for: Fits when OBS is the audio hub for live streaming or recordings.
NoiseTorch
open-sourceOpen source Linux app that applies RNNoise-based suppression to microphone input in real time.
RNNoise-based, low-latency on-device denoising with live monitoring and quick bypass testing.
NoiseTorch provides a real-time DSP pipeline for microphone audio, with a bypass option to compare processed vs unprocessed input during calls. The project supports configurable parameters that affect denoising intensity and behavior across speaking sessions. That control surface can match workflows where ambient conditions change often, and where users want repeatable local processing. It also fits teams that prefer deterministic behavior and fewer external dependencies than Adobe Podcast Enhance automation or Auphonic render jobs.
A key tradeoff is that NoiseTorch lacks the managed governance layer that enterprise-grade voice tools provide, so consistent rollout across many users requires manual setup per workstation. It fits situations where a single operator needs immediate mic cleanup for live meetings, streaming, or recording with minimal processing delay. It is less suitable when an organization needs centralized policy management or audit-friendly controls for who processed which audio.
- +Local inference reduces dependence on network availability
- +Real-time monitoring and bypass support quick A B comparisons
- +Virtual audio routing enables use in any conferencing app
- +Parameter control supports tuning for different microphones
- –Setup and routing require attention to OS audio device selection
- –No centralized admin or policy management for multi-user rollouts
- –Works best when CPU headroom is available for continuous processing
- –No built-in post-session mixing or broadcast-style mastering tools
Remote meeting participants
Live calls with fan and HVAC noise
Cleaner audio during meetings
Streamers and creators
Mic recording while gaming
Less distracting background noise
Show 2 more scenarios
Small production teams
Quick takes for podcasts
Faster edit cleanup
Local denoising reduces keyboard clicks and room noise before any manual edits.
Tinkerers and engineers
Audio pipeline experimentation
Tunable results per workstation
Configurable behavior supports repeatable tests across microphone models and environments.
Best for: Fits when individual users need real-time mic denoising with local processing for calls and streaming.
Adobe Podcast Enhance Speech
vertical specialistAI speech enhancement removes background noise and reverberation from recorded voice audio.
Speech-focused enhancement pipeline that targets intelligibility over system-level mic replacement behavior.
Adobe Podcast Enhance Speech focuses on denoising for spoken audio workflows, with emphasis on removing steady background noise during recording sessions. It processes voice and ambience together, producing cleaner intelligibility without forcing manual spectral edits.
The workflow centers on an audio enhancement pipeline geared for podcast-style speech, with preset-like behavior instead of granular mic-control. In comparison to Krisp and Auphonic, it targets speech clarity as a post-production-like step rather than a general system-wide noise remover.
- +Speech-first enhancement workflow tuned for podcast recording cleanup
- +Consistent output that reduces the need for manual gating tweaking
- +Quiet background reduction without requiring complex DSP parameter control
- +Straightforward try-run workflow that fits typical episode production
- –Less suitable for live conferencing because it is not positioned as real-time mic replacement
- –Limited control over denoising aggressiveness and VAD behavior
- –Does not target acoustic echo cancellation or dereverberation as a primary use case
- –Workflow is optimized for voice and may underperform on mixed-source audio
Best for: Fits when podcast teams need repeatable speech noise reduction for recorded episodes.
Waves Clarity Vx
vertical specialistVoice denoising plugins reduce background noise from speech recordings and live audio workflows.
Clarity Vx preset modes that emphasize speech clarity while limiting damage to consonants through selective spectral reduction.
Waves Clarity Vx targets mic background noise by inserting a real-time denoising stage into the audio chain with Waves plugin formats. It provides spectral processing control and multiple noise-reduction modes aimed at reducing HVAC rumble and keyboard clicks without flattening speech, and it supports VST and AU plugin deployment.
The workflow is largely preset-driven, so outcomes depend on selecting the right listening point in the signal chain and tuning VAD and reduction strength for the room. Clarity Vx is best assessed in a live monitoring setup because denoising latency and bypass behavior affect mic timing and intelligibility.
- +VST and AU deployment fits common DAW and studio chains
- +Preset-driven modes speed setup for typical speech cleanup
- +Spectral controls help reduce steady background noise
- +Works well when monitored closely during tuning
- –Latency sensitivity can require different settings for live use
- –Room tuning is needed to avoid speech artifacts
- –Automation depth depends on host support for parameter writes
- –Bypass behavior varies by host routing
Best for: Fits when podcast and studio workflows need consistent mic denoising inside existing DAW chains.
Descript Studio Sound
SMBAI audio enhancement reduces background noise and improves spoken-word recordings.
Denoising effect management directly tied to Descript speech editing sessions, enabling consistent reprocessing during revisions.
Descript Studio Sound applies mic cleanup inside the Descript editing workflow so denoising stays coupled to the rest of a voice production project. It focuses on background noise reduction for recorded speech and supports iterative improvements across revisions without rebuilding a separate processing pipeline.
The workflow is oriented around voice editing rather than configuring a standalone real-time DSP chain, which suits post-production timelines. It is best when mic noise varies between takes and editors want consistent results while trimming, removing, or rewriting segments in the same workspace.
- +Noise reduction stays in the same editing workflow as speech edits
- +Iterative take-by-take improvements support quick revision cycles
- +Works well when noise characteristics change across a recording
- +Results remain easy to reproduce when editors reapply the effect
- –Not designed for real-time low-latency mic monitoring use cases
- –Fine-grained controls for noise profiling are limited versus engineering tools
- –Less suitable for broadcast workflows needing explicit latency budgeting
- –Export behavior depends on the Descript project pipeline
Best for: Fits when podcasters and editors want mic background noise reduction inside a single speech editing workflow.
Utterly
SMBA macOS application reduces microphone background noise during calls and recordings.
Live denoising that keeps a stable monitoring path for mic capture without switching to an offline render loop.
Utterly focuses on mic background noise reduction through an always-on desktop denoising workflow designed for live voice capture. Its core capability is turning a noisy input signal into a cleaner feed with minimal operator intervention during calls and recordings.
Configuration centers on noise suppression strength and monitoring behavior rather than offline batch processing. For teams comparing real-time DSP pipelines, Utterly’s value comes from how predictably it keeps a conferencing-friendly audio path during speech-heavy sessions.
- +Low-friction setup for live mic cleanup during meetings
- +Adjustments focus on suppression behavior without complex tuning
- +Consistent denoising while speaking and when room noise rises
- +Works well for keyboard click and HVAC-style steady noise
- –Limited control over advanced processing stages like spectral gating
- –No documented API or webhook surface for provisioning workflows
- –Less suitable for broadcast-grade pipelines needing strict QA controls
- –May introduce noticeable artifacts on soft speech passages
Best for: Fits when creators or small teams need live mic cleanup with minimal configuration for conferencing-style capture.
CrystalSound
SMBAI audio software filters background noise from microphones and voice recordings.
Real-time adjustment workflow with continuous monitoring to lock in a stable suppression level for recurring rooms.
CrystalSound targets mic background noise reduction with a focus on workflow simplicity for conferencing and voice capture. It runs as an audio processing experience with configurable noise-suppression intensity and listening checks, rather than requiring deep DSP assembly.
The practical differentiator is its emphasis on consistent noise removal across typical room noise patterns with a small set of operational controls. CrystalSound is best assessed on latency feel, output clarity, and how quickly settings stabilize for recurring environments.
- +Quick tuning loop with audible monitoring while adjusting suppression strength
- +Predictable behavior across common office and home background noise
- +Low-friction setup for teams that want noise reduction without DSP tuning
- +Clear preset-like control flow for returning to known-good settings
- –Limited visibility into VAD threshold behavior and noise-floor measurement
- –Not built for SDK-level integration into an existing real-time DSP pipeline
- –Fewer configuration hooks for specialized audio chain requirements
- –No documented multi-mic or duplex routing controls for advanced scenarios
Best for: Fits when small teams need consistent mic noise reduction for calls and recordings without DSP plumbing.
Supertone Clear
vertical specialistAI voice processing removes noise and improves speech clarity during audio production.
Ambient profile fingerprinting to adapt denoising settings as room noise changes during a session.
Supertone Clear processes microphone input to reduce background noise in voice recordings and live calls. Its core capability is audio cleanup with adjustable denoising strength plus automatic profiling of ambient noise so the system can adapt to changing rooms.
The workflow centers on selecting a mode and monitoring results, then applying the cleaned output for downstream conferencing or recording. Compared with competitors that emphasize deep plugin ecosystems, Supertone Clear focuses on faster setup for typical mic noise scenarios.
- +Quick mic noise reduction workflow with clear mode selection
- +Adaptive ambient profiling reduces noise when room conditions shift
- +Consistent output quality for common HVAC and keyboard background
- +Low operational overhead during continuous use
- –Limited control surface for fine-grained denoising tuning
- –Less suited to complex scenarios needing acoustic echo cancellation
- –Plugin-style deployment options are not its primary strength
- –Does not target dereverberation performance as aggressively as dedicated audio tools
Best for: Fits when teams need fast mic background cleanup for calls and recordings without extensive audio routing work.
iZotope RX
vertical specialistAudio repair software includes denoise, voice de-rustle, de-wind, and dialogue cleanup tools.
Spectral Repair and spectral editing workflows for sculpting noise and transient artifacts beyond generic denoise
iZotope RX fits editors and audio engineers who need offline mic cleanup with fine control over different noise sources. It combines spectral processing, dialogue-oriented restoration tools, and flexible noise profiling so the denoising behavior can be tuned to a specific room and mic.
RX targets reduction of both steady noise and intermittent artifacts through separate modules instead of a single one-knob denoise. It also supports common plug-in workflows so denoised audio can be routed into a larger production chain.
- +Spectral editing makes it possible to target specific noise components in a waveform display
- +Multiple restoration modules allow different handling for hiss, hum, clicks, and broadband noise
- +Noise profiling workflows help match attenuation to the captured mic environment
- +VST and AU plug-in support fits denoising inside common DAW routing
- –Tuning spectral parameters can take time versus one-click mic noise reducers
- –Not optimized for real-time conferencing latency constraints in typical usage
- –Strong results depend on good noise-only capture and consistent mic placement
- –Automation controls are less straightforward than API-driven mic background reduction tools
Best for: Fits when offline dialogue cleanup needs repeatable spectral control across edits and exports.
Conclusion
After evaluating 10 music and audio, Dolby On stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right mic background noise reduction software
Mic background noise reduction software covers engines that run inside a live mic capture chain, like Dolby On, and engines that attach as filters, like OBS Noise Suppression and Waves Clarity Vx. This guide also includes studio-oriented workflows like Auphonic-style batch processing behavior, plus DAW and editing-centric tools such as Adobe Podcast Enhance Speech and iZotope RX.
The selection emphasis stays on integration depth, the control surface exposed for denoising behavior, and practical workflow fit across live capture, conference-style monitoring, and offline restoration. The tools covered span local RNNoise-style real-time inference paths, preset-driven enhancements, and spectral editing approaches used to reshape noise and transient artifacts.
Mic background noise reduction software for intelligibility-first denoising in live capture or offline repair
Mic background noise reduction software reduces audible background noise captured by a microphone using real-time DSP pipelines, offline restoration modules, or speech-focused enhancement stages. Dolby On targets intelligibility during capture with speech-first real-time tuning, while OBS Noise Suppression applies RNNoise inference directly inside the OBS audio filter chain.
Some tools act as workflow effects that stay coupled to editing sessions, as Descript Studio Sound manages denoising alongside speech edits, while others separate the task into restoration passes, as iZotope RX uses spectral repair and spectral editing modules for targeted noise and transient cleanup. The practical differences show up in whether denoising behavior is scoped to a specific host app like OBS, or delivered as DAW-compatible plugins like Waves Clarity Vx, or designed as speech-centric enhancement for recorded output like Adobe Podcast Enhance Speech.
Mic background noise reduction features that determine real-world intelligibility
The most measurable difference comes from whether the denoiser is speech-first or noise-agnostic, because Dolby On prioritizes intelligibility during capture rather than trying to make the room ambience uniform. In contrast, Adobe Podcast Enhance Speech targets recorded podcast cleanup with an enhancement pipeline that emphasizes repeatable intelligibility rather than acting like a live mic replacement for conferencing.
Speech-first tuning versus generic suppression
Dolby On uses speech-focused real-time capture tuning that keeps consonant intelligibility higher than generic noise gates. Adobe Podcast Enhance Speech uses a speech-oriented enhancement pipeline that improves recorded speech consistency without trying to replace live conferencing mic behavior.
Where the denoiser runs in the audio chain
OBS Noise Suppression runs directly as an OBS audio filter using RNNoise inference, so the effect is scoped to OBS capture graphs. Waves Clarity Vx delivers preset-driven denoising modes in common DAW plugin chains, so placement depends on the studio routing rather than a live capture app.
Live monitoring support and bypass iteration
NoiseTorch provides low-latency on-device denoising with live monitoring and quick bypass testing for A B comparisons. Dolby On also includes a low-latency capture mode for real-time speaking turns, but its advanced parameter control is more limited than Auphonic-style batch pipelines.
Control depth for denoising behavior
Dolby On limits advanced parameter control compared with batch-first toolchains, so teams that need threshold-like tuning may hit a ceiling. NoiseTorch exposes a workflow optimized around on-device real-time behavior and quick monitoring checks, while CrystalSound emphasizes a stable suppression adjustment loop with continuous monitoring.
Workflow coupling to editing or session iteration
Descript Studio Sound ties noise reduction to Descript speech editing sessions so denoising stays in the same editing workflow during revisions. iZotope RX stays decoupled from real-time capture and instead offers spectral repair and spectral editing modules for offline dialogue cleanup across exports.
Room adaptation and scene change handling
Supertone Clear uses ambient profile fingerprinting that adapts denoising settings as room noise changes during a session. CrystalSound provides continuous monitoring so recurring office and home backgrounds produce predictable suppression strength across the session.
Pick the right noise reduction engine for the capture and editing path
The decision hinges on whether the denoiser must operate inside a specific host app or must integrate into a DAW or editing workflow. OBS Noise Suppression is constrained to OBS unless routing is added, while Waves Clarity Vx fits common studio plugin chains where the mic signal already passes through VST or AU inserts.
Start with the audio host that must stay in the loop
If the audio hub is OBS, choose OBS Noise Suppression because RNNoise inference runs inside the OBS audio filter chain without building a separate capture path. If the production chain is DAW-centric, choose Waves Clarity Vx because preset-driven modes are designed to sit inside existing studio plugin workflows.
Decide whether intelligibility is the primary target for live speech turns
Choose Dolby On when live mic audio must keep intelligibility during real-time speaking turns with a speech-first capture tuning approach. Choose NoiseTorch when individual users need local on-device denoising with live monitoring and quick bypass testing to confirm improvement for calls and streaming.
Branch for offline restoration needs that justify spectral control time
Choose iZotope RX when offline dialogue cleanup must target specific noise components and transient artifacts using spectral repair and spectral editing modules. Choose Descript Studio Sound when denoising must remain coupled to a speech editing workflow so revisions reapply noise reduction inside the same editing session.
Match denoising behavior to how stable the room background is
Choose Supertone Clear when ambient conditions shift during a session because ambient profile fingerprinting adapts denoising settings as room noise changes. Choose CrystalSound when recurring backgrounds must produce a stable suppression level using continuous monitoring for quick adjustment loops.
Validate control needs for transient artifacts and gating-style behavior
Choose Dolby On carefully when keyboard clicks and other transients must be preserved, because keyboard click attenuation can become overly smoothed with its speech-first denoising approach. Choose tools with a more explicit monitoring and bypass iteration path, like NoiseTorch, when tuning must be validated quickly instead of relying on deeper parameter surfaces.
Who benefits from each noise reduction approach
Teams and creators need different behavior depending on whether the denoiser must act like a real-time replacement during conferencing and streaming or like an offline restoration module for edited episodes. A speech-first live tool fits when intelligibility is measured during speaking turns, while spectral repair fits when the deliverable is edited and exported.
OBS-first streamers and live producers
OBS Noise Suppression fits when RNNoise denoising must run inside OBS capture pipelines for live streaming and recording graphs without building additional routing.
Podcast teams producing repeatable recorded episodes
Adobe Podcast Enhance Speech fits when a speech-focused enhancement pipeline must keep recorded dialogue intelligible with less manual gating tweaking than ad hoc suppression. Descript Studio Sound fits when revisions stay inside one editing session so noise reduction reprocesses with speech edits.
Independent creators needing fast live monitoring checks
NoiseTorch fits when on-device denoising supports live monitoring and bypass comparisons for calls and streaming without depending on a network path.
Editors who need spectral sculpting beyond generic denoise
iZotope RX fits when hiss, hum, clicks, and broadband noise must be handled with spectral repair and module-based restoration in offline workflows.
Small teams using the same mic setup across recurring room conditions
CrystalSound fits when continuous monitoring aims for predictable suppression strength in office and home backgrounds. Supertone Clear fits when room noise shifts during the session and adaptive ambient profiling must adjust denoising settings.
Common mic noise reduction mistakes that reduce intelligibility
Mistakes usually come from mismatching scope and latency expectations or from using speech-first tools on content types that demand transient preservation. A second failure mode comes from expecting DAW plugin behavior to work the same way as live capture filters when host application routing differs.
Choosing a tool scoped to one host and expecting it to affect the whole system
OBS Noise Suppression only applies inside OBS, so non-OBS apps need routing or alternatives to avoid “no effect” outcomes outside OBS.
Treating studio plugin presets as live-ready without checking latency behavior
Waves Clarity Vx can be sensitive to live latency, so settings that work in DAW playback can require different configuration for live use to prevent monitoring lag.
Over-optimizing for noise removal and smoothing transients that carry meaning
Dolby On can overly smooth keyboard clicks and other transients, so monitoring at the human decision points helps confirm the denoising output remains usable.
Using offline spectral cleanup in scenarios that demand real-time replacement behavior
iZotope RX is designed for offline dialogue cleanup with spectral editing control, and it is not optimized for real-time conferencing latency constraints in typical usage.
Assuming a fixed suppression strength when the room background changes mid-session
Supertone Clear adapts using ambient profile fingerprinting, while tools with limited visibility into noise-floor measurement may not maintain intelligibility when the room conditions shift.
How We Selected and Ranked These Tools
We evaluated each tool on denoising control fit for mic background noise reduction and on where the effect runs in the capture or editing pipeline. Features accounted for 40% of the scoring, ease and value each accounted for 30%, and the same scoring lens was applied across Dolby On, OBS Noise Suppression, and the other reviewed tools.
Dolby On placed highest because speech-first real-time capture tuning prioritizes intelligibility during live speaking turns and includes a low-latency capture mode that aligns with live monitoring expectations. Dolby On also stayed competitive on usability, while tools like OBS Noise Suppression were penalized for scope limits to OBS and iZotope RX was penalized for not being optimized for real-time conferencing latency constraints.
Frequently Asked Questions About mic background noise reduction software
Which tools work inside a live audio chain instead of offline editing?
How does Auphonic-style post-production differ from conferencing-oriented real-time noise reduction like Krisp?
What breaks if a denoiser is tuned for a steady noise floor but the room noise changes mid-session?
How should Dolby On and Adobe Podcast Enhance Speech be evaluated for speech intelligibility versus ambience cleanup?
Where does OBS Noise Suppression fall short compared with tools that offer plugin formats in DAWs?
How does plugin and deployment format affect setup time for Waves Clarity Vx versus iZotope RX?
When should NoiseTorch be used instead of a cloud-centric denoiser approach?
How do CrystalSound and Utterly differ in operator control during live sessions?
What data workflow issues appear when switching from Descript Studio Sound to an offline editor like iZotope RX?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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