Prepared for: C++23 + Swift music player design
Verified on: 2026-06-17
Scope: macOS music playback, digital signal processing, Apple Silicon acceleration, open-source audio projects, and AI-based enhancement.
The best architecture is a hybrid stack.
Swift / SwiftUI
→ AVAudioEngine / Core Audio output
→ C++23 real-time DSP kernel
→ Accelerate / vDSP for filters, FFT, convolution
→ optional libebur128, soxr, FFmpeg
→ optional Core ML / MLX for offline AI features
Use Apple-native APIs for playback, output routing, device negotiation, and app integration.
Use C++23 for the real-time digital signal processing kernel.
Use selected open-source libraries only where they provide measurable value.
Do not make AI enhancement the default path.
Use AI for offline restoration, stem separation, denoise preview, tagging, and low-quality recording enhancement.
Use classical digital signal processing for equalizer, loudness normalization, limiter, sample-rate conversion, room correction, crossfeed, and true-peak control.
DSP means digital signal processing. It is math applied to audio samples.
PCM means pulse-code modulation. It is the common raw digital audio format.
FFT means Fast Fourier Transform. It converts audio from time view to frequency view.
FIR means finite impulse response. It is a filter without feedback.
IIR means infinite impulse response. It is a filter with feedback.
dBFS means decibels relative to full digital scale. 0 dBFS is the digital clipping point.
dBTP means decibels true peak. It estimates peaks between digital samples.
LUFS means loudness units relative to full scale. It measures perceived loudness.
Core ML is Apple’s machine learning framework.
MLX is Apple’s open-source array framework for machine learning on Apple Silicon.
“Audiophile-grade” is not a formal engineering standard.
Use measurable transparency instead.
| Requirement | Target |
|---|---|
| DSP bypass | Bit-identical where possible |
| Internal processing | Float32 minimum |
| Offline analysis | Float64 recommended |
| Output clipping | 0 clipped samples |
| True-peak ceiling | -1.0 dBTP for lossy output paths |
| Loudness measurement | ITU-R BS.1770 / EBU R128 compatible |
| Sample-rate conversion | One conversion maximum per playback path |
| Real-time callback | No locks, allocation, file I/O, network I/O, or logging |
| User control | Pure Mode and Enhanced Mode |
| Regression tests | Null test, sweep test, clipping test, loudness test |
ITU-R BS.1770-5 defines algorithms for programme loudness and true-peak audio measurement.
AES loudness resources list ITU-R BS.1770-5 and AES streaming loudness recommendations.
Input file
→ Apple decoder or FFmpeg fallback
→ Float32 PCM buffer
→ optional sample-rate conversion
→ pre-gain
→ parametric EQ
→ loudness gain
→ true-peak-aware limiter
→ Core Audio output
For analysis:
Input file
→ decode to analysis buffer
→ loudness scan
→ peak scan
→ true-peak scan
→ waveform cache
→ spectrum cache
→ metadata database
Do all analysis outside the real-time render callback.
| Layer | Apple-native choice | Open-source choice | Recommendation |
|---|---|---|---|
| Audio output | Core Audio / AVAudioEngine | JUCE, miniaudio, PortAudio | Use Apple native. |
| Audio graph | AVAudioEngine | JUCE graph | Use Apple native for macOS-only apps. |
| Custom effect | Audio Unit version 3 | JUCE plug-in wrapper | Use AUv3 if Apple-only. |
| Real-time DSP | C++23 kernel | JUCE DSP, custom C++ | Use custom C++23. |
| Vector DSP | Accelerate / vDSP | Eigen, FFTW, custom SIMD | Use vDSP first. |
| Loudness scan | Custom code | libebur128 | Use libebur128. |
| Resampling | AVAudioConverter | soxr, libsamplerate, SSRC | Native first. Use soxr or SSRC for expert offline mode. |
| Codec expansion | AVFoundation | FFmpeg | Add FFmpeg only when needed. |
| AI inference | Core ML / MLX | PyTorch, ONNX Runtime | Use Core ML for shipping. Use MLX for research. |
| Apple Silicon feature | Use for music rendering | Practical recommendation |
|---|---|---|
| CPU performance cores | Real-time DSP | Use for EQ, limiter, gain, routing, crossfeed. |
| CPU efficiency cores | Background work | Use for scanning, metadata, waveform cache. |
| Accelerate / vDSP | Filters, FFT, convolution | Use for math-heavy DSP before writing custom SIMD. |
| Metal GPU | Visualizers, long convolution, batch analysis | Use outside the real-time callback first. |
| Neural Engine | Core ML inference | Use for offline or buffered AI features. |
| Unified memory | Shared CPU/GPU/ML data | Useful for large waveforms and ML models. |
| M5 GPU Neural Accelerators | GPU-backed AI paths | Useful for AI, not needed for normal playback. |
| Media engine | Video codecs | Not relevant to normal music playback. |
Apple’s M1 MacBook Air has an 8-core CPU, 7-core or 8-core GPU, and 16-core Neural Engine.
Apple says the M1 Neural Engine supports 11 trillion operations per second.
Apple announced M5 with a 10-core CPU, GPU Neural Accelerators, 16-core Neural Engine, and 153 GB/s unified memory bandwidth.
For EQ, limiter, and loudness gain, M1 is enough.
For AI stem separation and large local models, M5 gives more headroom.
| Feature | Use it? | Why it matters | Implementation tip |
|---|---|---|---|
| Pure Mode | Yes | Gives reference playback. | Bypass all DSP. |
| Float32 pipeline | Yes | Prevents internal clipping during gain and EQ. | Keep the live path in floating point. |
| Float64 analysis | Yes | Improves offline measurement precision. | Use for loudness and test tools. |
| Pre-gain | Yes | Creates headroom before EQ boosts. | Reduce gain before positive bands. |
| Parametric EQ | Yes | More controlled than fixed-band EQ. | Use stable biquad filters. |
| Limiter | Yes | Prevents clipping after EQ and gain. | Put it last. |
| True-peak meter | Yes | Finds inter-sample peaks. | Oversample the meter path. |
| Loudness normalization | Yes | Reduces volume jumps between tracks. | Use ITU-R BS.1770 / EBU R128. |
| Dither | Export only | Reduces quantization distortion. | Use only when reducing bit depth. |
| Oversampling | Selective | Reduces nonlinear artifacts. | Use around saturation or clipping only. |
| Room correction | Optional | Corrects speaker-room response. | Require measurements. Avoid blind boosts. |
| Crossfeed | Optional | Helps headphone listening. | Make it headphone-only. |
| Stereo widening | Risky | Can damage mono compatibility. | Keep bass mostly mono below 120 Hz. |
| AI enhancement | Optional | Can restore or invent content. | Make it offline and reversible. |
Resampling affects quality when input sample rate differs from output device rate.
| Path | Recommendation |
|---|---|
| Live playback | Prefer device-native rate or one Apple conversion. |
| Offline export | Use highest-quality converter settings. |
| Audiophile mode | Avoid unnecessary conversion. |
| Crossfade between rates | Convert once into the session rate. |
| AI model input | Convert a copy, not the live stream. |
Use AVAudioConverter first for live playback.
Use soxr or SSRC when you need more offline control.
soxr exposes phase response, preserved bandwidth, aliasing, and rejection parameters.
SSRC describes itself as an audiophile-grade sample-rate converter for PCM WAV files.
Treat project descriptions as claims until you test them in your own harness.
Use ITU-R BS.1770-compatible measurement.
Do not normalize by sample peak only.
Peak normalization does not track perceived loudness.
Recommended metadata:
| Field | Purpose |
|---|---|
| Integrated loudness | Overall perceived loudness. |
| Loudness range | Dynamic range estimate. |
| Sample peak | Highest sample value. |
| True peak | Reconstructed peak estimate. |
| Track gain | Gain for shuffled playback. |
| Album gain | Gain preserving album balance. |
Playback rule:
decoded audio
→ track or album gain
→ limiter
→ output
Use album gain for albums.
Use track gain for shuffled playlists.
Target a true-peak ceiling around -1.0 dBTP when the output path includes lossy encoding or device conversion.
For version 1, use a parametric EQ.
A parametric EQ gives frequency, gain, and Q control.
Q means filter sharpness.
Recommended EQ path:
pre-gain
→ high-pass filter if needed
→ low-shelf
→ parametric bands
→ high-shelf
→ limiter
Implementation tips:
| Issue | Fix |
|---|---|
| Clicks during slider movement | Smooth parameters over 5–50 ms. |
| Clipping after boosts | Add pre-gain. |
| Filter instability | Clamp frequency, gain, and Q. |
| Poor stereo image | Apply matched filters to left and right. |
| CPU spikes | Precompute coefficients outside callback. |
Use vDSP or a proven C++ biquad implementation.
Add sweep tests and impulse tests.
The limiter protects output quality after EQ and loudness gain.
Recommended first version:
| Parameter | Value |
|---|---|
| Ceiling | -1.0 dBFS or -1.0 dBTP |
| Lookahead | 1–5 ms |
| Release | 50–200 ms |
| Oversampling | 2x or 4x for true-peak mode |
| Position | Last DSP stage |
Avoid aggressive compression in default mode.
Add a separate “Gym Mode” for noisy rooms.
Gym Mode can use compression because fidelity is not the main goal.
Room correction can improve speaker playback more than changing the player engine.
It also carries higher risk than EQ.
| Technique | Benefit | Risk |
|---|---|---|
| Minimum-phase EQ | Low latency | Does not fix all time-domain problems. |
| Linear-phase FIR | Corrects phase and magnitude | Adds latency and pre-ringing. |
| Mixed-phase correction | Balances both | More complex design. |
| Partitioned convolution | Runs long filters efficiently | More complex buffering. |
| Multi-position correction | Better listening area | Needs multiple measurements. |
Practical rule:
measurement import
→ target curve
→ correction filter
→ limiter
→ output
Do not boost deep nulls.
Deep nulls are often caused by cancellation.
Boosting them wastes headroom and can stress speakers.
Use cuts before boosts.
AI can improve poor recordings.
AI can also invent content that was not in the source.
That conflicts with audiophile transparency.
| AI approach | Usefulness | Production recommendation |
|---|---|---|
| Music source separation | Remix, karaoke, analysis | Offline feature. |
| Audio super-resolution | Low-bandwidth sources | Offline preview only. |
| AI denoise | User recordings | Not default for mastered music. |
| Differentiable DSP | Controllable neural effects | Research path. |
| Neural audio codecs | Compression research | Not v1 playback. |
| Audio tagging | Library intelligence | Good background feature. |
Open-Unmix is a peer-reviewed open-source reference implementation.
Hybrid Transformer Demucs reports stronger separation results than older Demucs variants.
AudioSR is a diffusion-based audio super-resolution model.
DDSP combines classical DSP blocks with neural networks.
For your product, put these under “AI Lab Mode.”
Do not put them in default playback.
| Option | Best use | Notes |
|---|---|---|
| Core ML | Shipping models inside macOS app | Uses CPU, GPU, and Neural Engine. |
| coremltools | Convert PyTorch/TensorFlow models | Use during build or research flow. |
| MLX | Apple Silicon research and prototypes | Supports Python, C++, C, and Swift APIs. |
| PyTorch MPS | Training and experiments | Good for research, not final app runtime. |
| Metal Performance Shaders Graph | Custom graph compute | Use when Core ML is not enough. |
Use Core ML when you ship.
Use MLX when you experiment locally.
Do not run Core ML inference inside the real-time callback.
| Rule | Reason |
|---|---|
| No heap allocation | Allocation can block. |
| No locks | Lock contention causes dropouts. |
| No file I/O | Disk latency is not bounded. |
| No network I/O | Network latency is not bounded. |
| No logging | Logging can lock or allocate. |
| No Core ML inside callback | Inference latency is not guaranteed. |
| No Swift object churn | Reference counting adds unpredictable work. |
| Use preallocated buffers | Keeps render time bounded. |
| Use lock-free parameter exchange | Keeps UI updates safe. |
RealtimeWatchdog checks for unsafe Core Audio thread activity.
It flags locks, memory allocation, Objective-C use, file I/O, and network I/O.
Use it as a development guardrail.
| Test | Method | Pass condition |
|---|---|---|
| Bypass null test | Output minus input | Near-zero residual where no conversion occurs. |
| Frequency response | Sweep or impulse | Matches target curve. |
| EQ stability | Parameter sweep | No clicks or unstable filters. |
| Clipping test | Full-scale sine and music | 0 clipped samples. |
| True-peak test | Oversampled meter | Peak below ceiling. |
| Loudness test | ITU-R BS.1770 vectors | Matches reference library. |
| Resampler test | Sweep conversion | No obvious aliasing or passband ripple. |
| CPU test | 128-frame buffer | No underruns under UI load. |
| Latency test | Loopback measurement | Document measured latency by device. |
| AI test | A/B comparison | AI must be optional and reversible. |
Use objective tests first.
Then run listening tests.
Do not rely on listening alone.
| Mode | Processing | User promise |
|---|---|---|
| Pure Mode | Decode → output | We do not change the sound. |
| Enhanced Mode | EQ → loudness gain → limiter | We improve consistency and prevent clipping. |
| Room Mode | Room correction → limiter | We correct speaker-room response from measurements. |
| Headphone Mode | Crossfeed → EQ → limiter | We reduce hard stereo separation. |
| AI Lab Mode | Offline AI tools | Experimental restoration and separation. |
For version 1, build:
Pure Mode
Enhanced Mode
Analysis Mode
Add Room Mode after measurement import works.
Add AI Lab Mode after you build A/B comparison and rollback.
| Phase | Build |
|---|---|
| 1 | SwiftUI app, playlist, playback, AVAudioEngine output |
| 2 | C++23 DSP kernel with bypass, gain, limiter |
| 3 | Parametric EQ using biquad filters |
| 4 | Offline loudness scan using libebur128 |
| 5 | Waveform and spectrum cache using vDSP FFT |
| 6 | True-peak scan and clipping reports |
| 7 | Optional soxr or SSRC offline resampling |
| 8 | Room correction import using FIR filters |
| 9 | Core ML or MLX offline AI experiments |
| 10 | AUv3 plug-in version for Logic Pro or GarageBand support |
| Component | Choice | Reason |
|---|---|---|
| UI | SwiftUI | Native macOS UI. |
| Audio graph | AVAudioEngine | Native graph and output path. |
| Output | Core Audio through Apple APIs | Best Apple platform integration. |
| DSP core | C++23 | Predictable real-time code. |
| Math acceleration | Accelerate / vDSP | Apple-optimized vector DSP. |
| Loudness | libebur128 | Implements EBU R128 style measurement. |
| Codec fallback | FFmpeg | Use only when Apple APIs miss a format. |
| Resampling expert mode | soxr or SSRC | Better control for offline conversion. |
| AI shipping | Core ML | Best Apple app deployment path. |
| AI research | MLX | Apple Silicon research framework. |
This section was added on 2026-06-17.
Maintenance was judged using recent releases, visible repository activity, package-manager updates, security response, and project adoption.
Community signal was judged using GitHub visibility, ecosystem use, packaging availability, and use by other audio projects.
| Library | Category | Maintenance signal | Community signal | Recommendation for your player |
|---|---|---|---|---|
| JUCE | C++ audio app and plug-in framework | GitHub releases show JUCE 8.x releases, including 8.0.12 in the current release feed. | Used across commercial and open-source audio plug-ins. Supports VST3, AU, AUv3, AAX, and LV2. | Use only if cross-platform release or plug-in hosting is a product goal. |
| miniaudio | C/C++ playback, capture, decoding, and mixing | Latest release page showed v0.11.25 on 2026-03-04. | Single-file design, no external dependencies, and broad desktop/mobile platform support. | Good fallback for tools or cross-platform experiments. For Apple-only output, use AVAudioEngine/Core Audio. |
| FFmpeg | Codec, container, conversion, and filter stack | Official FFmpeg page announced 8.1 “Hoare” on 2026-03-16. Download page also showed maintained release branches. | Industry-standard multimedia stack with broad codec/container support. | Use as a fallback for formats not covered by Apple APIs. Check LGPL/GPL build configuration. |
| Faust | DSP language and C++ code generation | GitHub release page showed recent 2.8x releases. Faust-to-CLAP work appeared in current research and tooling. | Used for DSP prototyping, code generation, and audio research. | Use to prototype EQ, filters, and effects, then port or generate C++ for the DSP kernel. |
| Rubber Band Library | Time-stretching and pitch-shifting | Official page announced v4.0 on 2024-10-25. | Used by audio/video tools for tempo and pitch changes. | Use for optional tempo and pitch features. Keep it out of default hi-fi playback. |
| KissFFT | Fast Fourier Transform | GitHub and distribution metadata show 131.x packaging, including 131.2.0 in Fedora/Arch metadata. | Small, portable, BSD-licensed FFT library. | Use as a portable FFT fallback. On Apple Silicon, prefer Accelerate/vDSP first. |
| PFFFT | FFT and fast convolution | Repository and package metadata show active packaging updates in 2026. | Small FFT library with ARM NEON support, including Apple Silicon. | Good portable FFT/convolution fallback if you do not want to depend only on vDSP. |
| r8brain-free-src | Sample-rate conversion | Repository and documentation were current enough to be useful; project had 2026 issue activity. | Known in audio-development communities for high-quality resampling. MIT license helps commercial apps. | Use for offline or expert-mode sample-rate conversion. For live playback, start with AVAudioConverter. |
| dr_libs | WAV, FLAC, MP3 single-file decoders | vcpkg listed 2026 package versions. Security issues were discussed publicly in 2026, with fix guidance to update to latest master. | Common in games, tools, and lightweight C/C++ projects. | Use for test tools or simple import paths. Avoid old versions for untrusted files. |
| Essentia | C++ audio and music information retrieval | PyPI showed a 2026 release for the Python package backed by the C++ library. | Used in MIR research and music-analysis tooling. MIR means music information retrieval. | Useful for offline analysis. AGPLv3 license can be incompatible with closed-source commercial apps. |
| Library | Category | Maintenance signal | Why still useful | Recommendation |
|---|---|---|---|---|
| libebur128 | Loudness and true-peak measurement | Latest GitHub release was older, around 2021. | EBU R128 and ITU-R BS.1770 are stable standards. | Use with regression tests against known loudness vectors. |
| libsamplerate | Sample-rate conversion | Latest GitHub release shown was 0.2.2 from 2021. | Mature library with BSD-style licensing. | Fine for tooling. Prefer r8brain, soxr, or AVAudioConverter for production decisions. |
| soxr | Sample-rate conversion | Upstream release cadence is old, but it remains packaged and downloaded. | Exposes phase, bandwidth, aliasing, and rejection controls. | Good for offline expert conversion after you test it. |
| libsndfile | WAV, AIFF, and sampled audio file I/O | Latest GitHub release shown was 1.2.2 from 2023-08-13. | Mature and widely packaged. | Use for test tools and offline exports. |
| RtAudio | Cross-platform real-time audio I/O | Official latest release was 6.0.1 from 2023-08-01. | Stable cross-platform API for ALSA, JACK, PulseAudio, OSS, macOS, and Windows APIs. | Use only if you need cross-platform audio I/O. Do not use for Apple-only output. |
| zita-convolver | Convolution | Older cadence and GPL license. | Useful technical design for partitioned convolution. | Use as reference or in GPL-compatible products. |
For your Apple Silicon C++23 + Swift player:
| Need | Pick | Why |
|---|---|---|
| macOS output | AVAudioEngine / Core Audio | Native device integration and routing. |
| Real-time DSP | Your C++23 engine | Keeps the render path predictable. |
| Vector math / FFT | Accelerate / vDSP | Apple-optimized path for Apple Silicon. |
| Portable FFT fallback | PFFFT or KissFFT | Small and portable. |
| EQ/filter prototyping | Faust | Fast DSP iteration and C++ generation. |
| Loudness scan | libebur128 | Implements the standard workflow. |
| Offline resampling | r8brain-free-src | MIT-licensed C++ resampler. |
| Codec fallback | FFmpeg | Broadest codec/container support. |
| Simple test decoders | dr_libs | Single-file WAV/FLAC/MP3 tooling. |
| Time-stretch/pitch | Rubber Band | Specialist library for tempo and pitch. |
| Offline music analysis | Essentia | Strong analysis coverage, but check AGPLv3. |
| Cross-platform app future | JUCE | Full app and plug-in ecosystem. |
Do not replace Apple’s audio output path with a cross-platform library unless cross-platform release is required.
Use this stack first:
SwiftUI
→ AVAudioEngine / Core Audio
→ C++23 DSP kernel
→ Accelerate / vDSP
→ libebur128
→ r8brain-free-src
→ FFmpeg fallback only when needed
Use JUCE later only if you decide to ship Windows/Linux builds or plug-in formats.
For an Apple-only music player:
Apple native output
+ C++23 real-time DSP
+ Accelerate / vDSP
+ selected open-source libraries
+ optional offline AI
This gives the best balance between fidelity, control, latency, power efficiency, and maintenance.
Do not enhance every track by default.
Make every processing step measurable, visible, and reversible.
The links below were checked against official pages, standards pages, paper pages, or original repositories on 2026-06-17.
-
Apple AVAudioEngine documentation
https://developer.apple.com/documentation/avfaudio/avaudioengine -
Apple AVAudioConverter documentation
https://developer.apple.com/documentation/avfaudio/avaudioconverter -
Apple Technical Note TN3136: AVAudioConverter sample-rate conversion
https://developer.apple.com/documentation/technotes/tn3136-avaudioconverter-performing-sample-rate-conversions -
Apple Core ML documentation
https://developer.apple.com/documentation/coreml -
Apple Core ML overview
https://developer.apple.com/machine-learning/core-ml/ -
Apple Accelerate framework
https://developer.apple.com/documentation/accelerate -
Apple vDSP audio unit sample
https://developer.apple.com/documentation/accelerate/creating-an-audio-unit-extension-using-the-vdsp-library -
Apple FFT documentation
https://developer.apple.com/documentation/accelerate/fast-fourier-transforms -
Apple Metal overview
https://developer.apple.com/metal/ -
Apple Metal Performance Shaders
https://developer.apple.com/documentation/metalperformanceshaders -
Apple Metal Performance Shaders Graph
https://developer.apple.com/documentation/metalperformanceshadersgraph -
Apple PyTorch MPS backend page
https://developer.apple.com/metal/pytorch/ -
Apple AUv3 custom audio effects sample
https://developer.apple.com/documentation/avfaudio/creating-custom-audio-effects -
Apple signal generator sample
https://developer.apple.com/documentation/avfaudio/building-a-signal-generator -
Swift C++ interoperability
https://www.swift.org/documentation/cxx-interop/ -
Apple M1 announcement
https://www.apple.com/newsroom/2020/11/apple-unleashes-m1/ -
Apple M1 MacBook Air technical specification
https://support.apple.com/en-lk/111883 -
Apple M5 announcement
https://www.apple.com/newsroom/2025/10/apple-unleashes-m5-the-next-big-leap-in-ai-performance-for-apple-silicon/ -
Apple M5 MacBook Air technical specification
https://support.apple.com/en-lk/126320
-
ITU-R BS.1770-5: loudness and true-peak measurement
https://www.itu.int/rec/R-REC-BS.1770 -
ITU-R BS.1770-5 PDF
https://www.itu.int/dms_pubrec/itu-r/rec/bs/R-REC-BS.1770-5-202311-I!!PDF-E.pdf -
AES loudness references and standards list
https://aes.org/resources/audio-topics/loudness-project/resources-and-references/ -
EBU R128 publication page
https://tech.ebu.ch/publications/r128/
-
libebur128
https://github.com/jiixyj/libebur128 -
soxr / SoX Resampler library
https://sourceforge.net/projects/soxr/ -
libsamplerate / Secret Rabbit Code
https://github.com/libsndfile/libsamplerate -
Shibatch Sample Rate Converter
https://github.com/shibatch/ssrc -
FFmpeg codecs documentation
https://ffmpeg.org/ffmpeg-codecs.html -
FFmpeg legal and license information
https://www.ffmpeg.org/legal.html -
JUCE framework
https://github.com/juce-framework/JUCE -
AudioKit
https://github.com/AudioKit/AudioKit -
RealtimeWatchdog
https://github.com/TheAmazingAudioEngine/RealtimeWatchdog
-
MLX official repository
https://github.com/ml-explore/mlx -
MLX official project page
https://opensource.apple.com/projects/mlx -
MLX unified memory documentation
https://ml-explore.github.io/mlx/build/html/usage/unified_memory.html -
coremltools
https://github.com/apple/coremltools -
coremltools PyTorch conversion workflow
https://apple.github.io/coremltools/docs-guides/source/convert-pytorch-workflow.html -
PyTorch MPS backend documentation
https://docs.pytorch.org/docs/stable/notes/mps.html
-
Open-Unmix JOSS paper
https://joss.theoj.org/papers/10.21105/joss.01667 -
Open-Unmix PyTorch repository
https://github.com/sigsep/open-unmix-pytorch -
Open-Unmix project page
https://sigsep.github.io/open-unmix/ -
Hybrid Transformer Demucs paper
https://arxiv.org/abs/2211.08553 -
Demucs repository
https://github.com/facebookresearch/demucs -
AudioSR paper
https://arxiv.org/abs/2309.07314 -
AudioSR project page
https://audioldm.github.io/audiosr/ -
AudioSR repository
https://github.com/haoheliu/versatile_audio_super_resolution -
DDSP OpenReview paper
https://openreview.net/forum?id=B1x1ma4tDr -
DDSP repository
https://github.com/magenta/ddsp -
Review of differentiable DSP for music and speech synthesis
https://doi.org/10.3389/frsip.2023.1284100 -
Room Response Equalization — A Review
https://www.mdpi.com/2076-3417/8/1/16 -
Real-time low-latency music source separation using Hybrid Spectrogram-TasNet
https://arxiv.org/abs/2402.17701 -
PEAQ reassessment paper
https://arxiv.org/abs/2212.01467
-
JUCE GitHub releases
https://github.com/juce-framework/JUCE/releases -
JUCE project repository
https://github.com/juce-framework/JUCE -
miniaudio GitHub releases
https://github.com/mackron/miniaudio/releases -
miniaudio official project page
https://miniaud.io/ -
FFmpeg official website
https://www.ffmpeg.org/ -
FFmpeg official download page
https://ffmpeg.org/download.html -
Faust GitHub releases
https://github.com/grame-cncm/faust/releases -
Faust project repository
https://github.com/grame-cncm/faust -
Rubber Band Library official page
https://breakfastquay.com/rubberband/ -
Rubber Band GitHub mirror
https://github.com/breakfastquay/rubberband -
KissFFT repository
https://github.com/mborgerding/kissfft -
KissFFT Fedora package metadata
https://rpmfind.net/linux/RPM/fedora/44/s390x/k/kiss-fft-131.2.0-1.fc44.s390x.html -
PFFFT repository
https://github.com/marton78/pffft -
PFFFT FreeBSD package metadata
https://www.freshports.org/math/pffft -
r8brain-free-src repository
https://github.com/avaneev/r8brain-free-src -
r8brain-free-src documentation
https://www.voxengo.com/public/r8brain-free-src/Documentation/ -
dr_libs repository
https://github.com/mackron/dr_libs -
drlibs vcpkg package metadata
https://vcpkg.io/en/package/drlibs.html -
dr_libs 2026 security advisory reference
https://github.com/marlinkcyber/advisories/blob/main/advisories/MCSAID-2026-001-dr-libs-heap-overflow.md -
RtAudio official page
https://caml.music.mcgill.ca/~gary/rtaudio/ -
Essentia repository
https://github.com/MTG/essentia -
Essentia PyPI release page
https://pypi.org/project/essentia/ -
libsndfile GitHub releases
https://github.com/libsndfile/libsndfile/releases/
- Apple sources were checked against Apple Developer, Apple Support, or Apple Newsroom pages.
- ITU and AES loudness references were checked against ITU and AES pages.
- Open-source projects were checked against original GitHub or SourceForge repositories.
- Peer-reviewed entries were checked against JOSS, Frontiers, MDPI, or OpenReview pages.
- arXiv papers were treated as research preprints unless a reviewed venue was also linked.
- Repository claims were not treated as proof of audio quality without independent testing.