MAEB: Massive Audio Embedding Benchmark

🍞 Hook: You know how your school report card shows many subjects—math, science, reading—so one A doesn’t mean you’re great at everything? AI that listens to sounds needs a similar kind of report card.

🥬 The Concept (Benchmark): A benchmark is a fair, shared test that checks what models can really do across many skills. How it works:

1. Pick representative tasks that reflect real jobs (like recognizing emotions or finding a barking dog sound).
2. Use the same scoring rules and data for everyone.
3. Compare results so we see strengths and weaknesses clearly. Why it matters: Without a shared, multi-skill test, we can’t tell if a model is brilliant in one corner but lost in others. 🍞 Anchor: Think of a track meet with sprints, long jump, and relays—one superstar sprinter might not win the whole meet.

The World Before: Audio AI had many cool tricks—speech recognition, music tagging, and sound event detection. But each group used its own tests, data, and scoring. Some benchmarks focused only on environmental sounds (like ESC-50). Others centered on speech or a single language. That meant we didn’t know if a model that did great on, say, bird calls would also understand people’s emotions in 10 languages—or fall apart.

The Problem: Researchers wanted to build general audio embeddings—compact summaries of audio that help with many tasks. But evaluations were fragmented. Different labs reported different numbers on different datasets with different settings. We couldn’t compare apples to apples, so progress felt confusing. Also, older benchmarks weren’t kept up-to-date, so they missed new models and tasks like cross-modal audio–text or multilingual setups.

🥬 The Concept (Audio Embedding): Imagine squishing a long audio clip into a short vector (a list of numbers) that still captures what matters. How it works:

1. Convert the waveform into features (often spectrograms).
2. Feed into a model to produce an embedding.
3. Use that embedding for tasks like classification, search, or clustering. Why it matters: Good embeddings make many tasks easier, faster, and more accurate. 🍞 Anchor: Like turning a whole movie into a short summary that still tells you who, what, and why.

Failed Attempts: Early broad attempts like HEAR were a big step, but still limited in scale, task diversity, and maintenance. Many other evaluations zoomed in on one domain (only speech or only environmental sounds) or one task type (only classification), leaving out essentials like zero-shot use (no training), cross-modal retrieval (text ↔ audio), or unsupervised clustering. Meanwhile, new model families (speech-pretrained vs. audio–text contrastive models vs. Audio LLMs) made the landscape even more complicated.

The Gap: The field needed a living, community-maintained benchmark—spanning speech, music, environmental sounds, bioacoustics; including English and 100+ other languages; and covering multiple task types (classification, zero-shot, clustering, retrieval, pair classification, reranking). It also needed to be efficient so labs with fewer GPUs could still participate.

🥬 The Concept (MTEB Ecosystem): MTEB is a shared house of rules, tools, and leaderboards for embeddings in text and images. MAEB extends it to audio. How it works:

1. Use standardized metrics and interfaces known from MTEB.
2. Add audio tasks with minimal extra code.
3. Store versioned results publicly so everyone can reproduce. Why it matters: Consistency and openness make progress real and trustworthy. 🍞 Anchor: It’s like using the same ruler and stopwatch for every sports day, every year, so records are fair and comparable.

Real Stakes: This matters in daily life. Voice assistants need to work across accents and languages. Music apps should find songs by humming. Safety systems should detect alarms or glass breaking in noisy rooms. Scientists want to track bird species and ecosystems. Customer support tools need to understand emotion and intent globally. A strong, fair benchmark helps us build models that do all these jobs—and shows where they still fail, especially for underrepresented languages and real-world noise.

In short, the world before MAEB was a patchwork. The paper’s contribution is to sew the pieces together into a single, sturdy quilt that covers the many ways AI listens and understands sounds.

🍞 Hook: Imagine trying to judge a superhero team by only watching the speedster run. You’d miss the flyer’s rescues and the hacker’s clever plans. Audio AI was often judged like that—one talent at a time.

🥬 The Concept (MAEB): MAEB is a massive, unified benchmark that tests audio embeddings across 30 tasks, 7 task types, 100+ languages, and multiple domains (speech, music, environmental sounds, bioacoustics, and more), built inside the trusted MTEB ecosystem. How it works:

1. Curate many diverse datasets for breadth (domains, languages, task types).
2. Standardize preprocessing, metrics, and evaluation code.
3. Rank models fairly (Borda count) and also report averages. Why it matters: A single, consistent yardstick reveals true strengths, weaknesses, and trade-offs—so we can actually make progress. 🍞 Anchor: It’s like a triathlon scoreboard that combines swimming, biking, and running—no more calling someone “the best” after just one event.

The Aha! Moment (one sentence): If we evaluate audio embeddings the same way across many realistic tasks and languages—efficiently and consistently—we finally learn what they’re good at, what they miss, and how to fix it.

Multiple Analogies:

1. Report Card: MAEB is a full report card, not just a single quiz, so we see well-rounded performance.
2. Universal Remote Tester: If you want a remote that works for TVs, speakers, and projectors, you must test all of them—MAEB does that for audio tasks.
3. Fitness Circuit: Models lift (classification), balance (clustering), sprint (retrieval), and do obstacle courses (cross-modal). Only a complete circuit shows overall fitness.

Before vs. After:

• Before: Benchmarks were narrow, scores didn’t transfer, and results weren’t easily comparable.
• After: MAEB gives a unified picture: no single model is best at everything; clustering is broadly hard; multilingual audio-text is especially weak; and embedding strength predicts Audio LLM performance.

🥬 The Concept (Contrastive Learning): You know how you learn better by comparing examples—what’s similar, what’s different? How it works:

1. Pair matching items (e.g., a sound and its caption) and push their embeddings closer.
2. Push mismatched pairs apart.
3. Repeat across huge datasets. Why it matters: It creates a shared space where audio and text can “meet,” enabling zero-shot classification and cross-modal search. 🍞 Anchor: Like learning friend groups: best friends (close) vs. strangers (far) on a seating chart.

🥬 The Concept (Cross-Modal Audio–Text Tasks): These are jobs where audio and text have to work together, like searching sounds with words. How it works:

1. Embed audio and text into the same space.
2. Compare them with a similarity score (like cosine).
3. Use the scores to classify or retrieve. Why it matters: Many real apps (find “dog barking” sounds) need text↔audio understanding. 🍞 Anchor: Typing “rainstorm” to find matching sound clips is cross-modal search.

🥬 The Concept (Multilingual Processing): Handling many languages, accents, and dialects. How it works:

1. Train on diverse languages.
2. Align representations so knowledge transfers across languages.
3. Test widely to ensure fairness and coverage. Why it matters: The world doesn’t talk in just one language; access and equity depend on multilingual AI. 🍞 Anchor: A voice assistant that understands grandma in Yoruba and your cousin in Japanese.

🥬 The Concept (Clustering): Grouping similar sounds without labels. How it works:

1. Compute embeddings.
2. Use an algorithm (like MiniBatchKMeans) to form groups.
3. Measure if groups match real categories (V-measure). Why it matters: Lets you organize huge sound libraries and discover patterns even when you have no labels. 🍞 Anchor: Auto-sorting your photo album into beaches, birthdays, and school events—except with sound.

Why It Works (intuition):

• Using many tasks prevents overfitting to one trick.
• A standardized pipeline removes tiny differences that can skew scores.
• Efficient task selection (filtering similar tasks; keeping language/diversity) keeps the signal while cutting costs.
• Borda ranking resists outliers and scale issues, making comparisons fair.

Building Blocks:

• Task families: classification, zero-shot, clustering, retrieval, pair classification, reranking.
• Diverse domains: speech, music, environmental/bioacoustic sounds.
• Language breadth: 100+ languages and dialects.
• Integration with MTEB: shared interfaces, metrics, and public artifacts.
• Scalable subsets: MAEB (30 tasks), MAEB(audio-only, 19 tasks), MAEB+ (full 98-task collection).

🥬 The Concept (Borda Count): A ranking method where each task “votes” on the model order, and points are added up. How it works:

1. Rank models per task.
2. Give points by position (higher = better rank).
3. Sum across tasks for the final standing. Why it matters: It’s robust to odd scales and outliers; a steady performer can win overall. 🍞 Anchor: Like a season-long league table where consistent teams finish higher than one-hit wonders.

Bottom line: MAEB brings the big picture into focus, revealing trade-offs and map directions for better, fairer, more universal audio understanding.

At a high level: Input (audio and/or text) → Standardized preprocessing → Embedding extraction → Task-specific evaluation (classification, zero-shot, clustering, retrieval, pair classification, reranking) → Aggregation and ranking (Borda + averages).

Step-by-step, like a recipe:

1. Collect and Curate Tasks

• What happens: The authors assemble 98 tasks (MAEB+) across domains and languages, then filter to 30 (MAEB) to keep it efficient but representative. They also provide a 19-task audio-only subset.
• Why this step exists: Too many tasks make evaluation expensive; too few make it unrepresentative.
• Example: Keep a unique bioacoustics clustering task even if it’s small, because nothing else covers it.

🥬 The Concept (Task Selection and Filtering): What it is: A principled way to trim 98 tasks to 30 while preserving diversity and reliability. How it works:

1. Validity: Pick directions that match real use (e.g., text→audio for search).

2. Unique coverage: Keep tasks that test rare skills or domains.

3. Linguistic breadth: Prefer tasks with more languages.

4. Redundancy removal: Drop tasks that rank models almost identically (high Spearman correlation) to another kept task.

5. Runtime efficiency: Choose cheaper tasks if all else is equal. Why it matters: Saves 2–3× GPU hours while keeping model rankings highly correlated with the full set. 🍞 Anchor: Like making a balanced 10-question quiz from a 30-question pool—remove duplicates, keep must-haves, and finish in time.

6. Standardized Preprocessing

• What happens: Truncate audio to ≤30s (or model limit), resample to model-specific rates (e.g., 16kHz for speech encoders, 48kHz for CLAP), convert to mono if needed.
• Why this step exists: Keeps things fair and within memory limits across very different models.
• Example: A speech model may only accept 30s at 16kHz—everyone follows that for comparability.

1. Embedding Extraction

• What happens: Use each model’s native pooling. • Transformers: mean-pool over time. • CNNs: global average pooling. • Contrastive audio–text models: use audio branch outputs with L2 normalization. • Audio LLMs: last-token pooling from the final layer.
• Why this step exists: Use models as intended to avoid accidental handicaps.
• Example: For CLAP, use its audio encoder head so it aligns well with text in retrieval tasks.

1. Task-specific Evaluations

• Classification (few-shot linear probe): • What: Train a simple logistic regression on top of embeddings with 8 examples per class. • Why: Tests how linearly separable the information is without heavy fine-tuning. • Example: Predict “angry” vs. “happy” from speech emotions on CREMA-D.

🥬 The Concept (Linear Probe): What it is: A tiny classifier that checks if embeddings already contain the needed info. How it works:

1. Freeze the embedding model.
2. Train a simple linear layer on a few labeled examples.
3. Measure accuracy. Why it matters: Reveals representation quality without letting a big head model do all the work. 🍞 Anchor: Like testing if your notes are so clear that a friend can ace the quiz with just a simple highlighter.

• Zero-shot Classification: • What: Match audio embeddings to class name prompts (e.g., “This is a sound of dog bark”). • Why: Tests open-vocabulary ability without any training on the target dataset. • Example: On ESC-50, choose among labels like “chainsaw” or “rain.”

🥬 The Concept (Zero-shot Classification): What it is: Classifying without task-specific training by comparing to text labels. How it works:

1. Embed the audio.
2. Turn class names into descriptive prompts and embed them.
3. Pick the class with the highest similarity. Why it matters: Real-world systems often must handle new labels on the fly. 🍞 Anchor: Identifying a new animal by matching it to a field guide picture—no special training needed.

• Clustering: • What: Use MiniBatchKMeans with k set to true class count; score with V-measure. • Why: Tests unsupervised structure—can the model group similar sounds by itself? • Example: Group songs by genre in GTZAN without labels.

• Retrieval (audio↔audio, text↔audio): • What: Rank items in a large set by cosine similarity to a query; evaluate with CV Recall@5 or nDCG@10. • Why: Mirrors real search (“find dog barks” or “find sounds like this”). • Example: Text→audio retrieval on Clotho; audio→text on AudioCaps.

🥬 The Concept (Retrieval): What it is: Finding the most relevant items to a query. How it works:

1. Embed query and all candidates.
2. Compute cosine similarities.
3. Sort and check if the true matches appear near the top. Why it matters: Powers search in media libraries, assistants, and archives. 🍞 Anchor: Typing “thunder” and instantly getting top-5 thunder clips.

• Pair Classification: • What: Judge if two audios match by a rule (same accent, same speaker, same class) using cosine similarity; measure average precision. • Why: Tests fine-grained similarity and verification. • Example: Are these two clips the same accent in VoxPopuli Accent Pair?

🥬 The Concept (Pair Classification): What it is: Deciding if two things belong together under a given rule. How it works:

1. Embed both clips.
2. Compute similarity.
3. Turn similarities into a precision-recall curve; summarize as average precision. Why it matters: Useful for verification, deduplication, and recommendation. 🍞 Anchor: Checking if two puzzles pieces fit the same spot.

• Reranking: • What: Given a small candidate set that already includes relevant items and hard negatives, rank them; measure MAP@1000. • Why: Tests nuanced discrimination when the shortlist is tough. • Example: Reorder candidate music genres after a first-stage filter.

🥬 The Concept (Reranking): What it is: A second pass that cleans up a tough shortlist. How it works:

1. Take a set with both good and tricky bad options.

2. Re-score with finer comparisons.

3. Evaluate how high true matches end up. Why it matters: Improves quality in search and recommendation pipelines. 🍞 Anchor: After picking your top 10 books, deciding the final reading order.

4. Aggregation and Ranking

• What happens: For each task, compute the model’s metric; aggregate across tasks into averages; compute a Borda rank where every task votes on model order.
• Why this step exists: Averages give performance magnitude; Borda gives robust, scale-invariant ordering.
• Example: A model that’s never the best but always near the top can win overall with Borda.

1. Efficiency and Reproducibility

• What happens: MAEB (30 tasks) preserves high correlation with the larger set (MAEB extended) while reducing GPU hours 2–3×. Code, metrics, and results are versioned and public.
• Why this step exists: Makes participation possible for more teams and keeps science transparent.
• Example: On an A100 GPU, CLAP-htsat-fused takes ~13h on MAEB vs. ~35h on the extended set.

Secret Sauce (what makes MAEB clever):

• Smart task filtering preserves the “signal” while trimming cost.
• Deep integration with MTEB gives stable interfaces and public leaderboards.
• Coverage that reflects reality: multilingual speech, long-form-aware truncation, cross-modal alignment tasks.
• Consistent, model-native embedding extraction avoids handicapping any architecture.

🥬 The Concept (Cross-Modal Alignment and Contrastive Models): What it is: Training audio and text together so their embeddings match when they describe the same thing. How it works:

1. Pair sounds with captions.
2. Pull matched pairs closer; push mismatches apart.
3. Repeat at scale so the space becomes robust. Why it matters: Enables zero-shot and retrieval across audio and text—core for many apps. 🍞 Anchor: Lining up a map (text) with the real city (audio) so streets match landmarks.

Altogether, MAEB’s methodology is a clean, fair conveyor belt: prepare data fairly, extract embeddings faithfully, test many ways, and summarize robustly—so we finally know what “good” really looks like for audio embeddings.

The Test: MAEB evaluates 53 models across 30 tasks in 7 categories, covering speech, music, environmental sounds, bioacoustics, and cross-modal audio–text in 100+ languages. Core metrics include accuracy (classification), V-measure (clustering), CV Recall@5 or nDCG@10 (retrieval), MAP@1000 (reranking), and average precision (pair classification). To stay efficient, the authors filtered 98 tasks (MAEB+) down to 30 while preserving ranking fidelity and diversity. A 19-task audio-only subset supports models without text components.

The Competition: Models span four families.

• Audio encoders (e.g., AST, CNN14, YAMNet, Wav2Vec2/XLS-R, WavLM, HuBERT, Data2Vec, Encodec)
• Sequence-to-sequence speech/translation models (e.g., Whisper, MMS, SeamlessM4T, SpeechT5 ASR)
• Contrastive audio–text models (e.g., CLAP, MS-CLAP, Wav2CLIP, MuQ-MuLan, SpeechT5 Multimodal)
• Large audio–language models adapted for embeddings (Qwen2-Audio, LCO-Embedding)

Scoreboard (with context):

• LCO-Embedding-Omni-7B ranks 1st overall by Borda count. It posts the highest average scores overall (about 52.2%), excels in cross-modal retrieval (~50.3%), and shines at zero-shot (~64.5%). That’s like getting an A across subjects, especially strong in the “text+audio” classes.
• Qwen2-Audio-7B ranks 2nd overall and 1st on audio-only tasks (average ~50.8%). It’s great at reranking (~80.8%) and stronger-than-most at clustering (~12.7%)—think of it as a very capable all-round athlete when text isn’t involved.
• Whisper-medium ranks 3rd overall, with robust audio-only performance (~48.2%) and strong classification (~51.7%)—but it can’t do cross-modal tasks (no text alignment), like an athlete who dominates running but can’t swim.
• CLAP variants (e.g., larger_clap_general; music_and_speech) are balanced for cross-modal abilities and dominate environmental sound understanding and open-vocabulary tasks, but trail on multilingual speech tasks. They’re like star fielders in baseball—amazing in the outfield but not the best pitchers.
• AST (Audio Spectrogram Transformer) often leads in music and environmental domains but lacks cross-modal support; great specialist, limited versatility.

Surprising and Key Findings:

1. No Universal Winner:

• Speech-trained models (e.g., Whisper) excel at speech tasks but stumble on music/environment.
• Audio–text contrastive models (e.g., CLAP) excel on environmental sounds and zero-shot but struggle on multilingual speech.
• LCO-Embedding and Qwen2-Audio, although both large multimodal models, differ drastically on cross-modal retrieval (≈50.3% vs. ≈1.6%), showing that scale and modality alone aren’t enough—training data and objectives matter.

1. Multilingual Gaps Are Large:

• On SIB-FLEURS and related multilingual tasks (100+ languages), high-resource languages can reach 40–60%+ accuracy while many low-resource languages stay near 20% or worse—even for the best models. That’s like being fluent in French and Spanish but barely recognizing words in Yoruba or Xhosa.
• Cross-modal multilingual retrieval is especially weak; in FLEURS retrieval (102 languages), even strong CLAP variants often score below 3% for many pairs, frequently under 1%. English strength does not transfer automatically across languages.

1. Acoustic vs. Linguistic Trade-off:

• Some models are great at acoustic cues (gender, timbre) but weak at linguistic cues (language ID), and vice versa (e.g., CLAP vs. Whisper patterns on VoxPopuli tasks). This suggests current encoders must choose what to pay attention to—and can’t do both equally well yet.

1. Clustering Is Hard for Everyone:

• Across the board, clustering scores are modest (e.g., a top model around the low 20%s on some settings). Many strong supervised models (great with labels) fail to organize the space well without labels. This is a red flag for tasks like library organization or discovery.

1. MAEB Embeddings Correlate with Audio LLM Performance:

• Preliminary results show a high correlation (R≈0.86, n=4) between MAEB+ embedding quality and downstream Audio LLM reasoning (MMAU). While based on few points, it hints that strong embeddings are not just academic—they help real multimodal systems.

Concrete Examples:

• LCO-Embedding dominates speech-text retrieval (CMU Arctic, EmoVDB, LibriTTS, HiFiTTS), suggesting excellent speech–text alignment.
• CLAP variants lead on environmental datasets like AudioCaps, AudioSetStrong, and Clotho (text↔audio), consistent with their training.
• In classification, Qwen2-Audio-7B attains top averages and shines on emotions (CREMA-D, IEMOCAP), music (GTZAN, Beijing Opera, Mridangam), and vocal sounds.

What breaks without MAEB:

• Without a broad, fair test bed, a team might pick a top model for English speech—and then fail at bird calls, music genres, or non-English accents in production. MAEB’s full picture prevents these surprises.

Bottom line: MAEB turns a messy scoreboard into a clear league table, revealing real gaps (multilingual, clustering) and pointing to what actually works (contrastive alignment for environmental sounds; strong speech–text alignment for speech retrieval).

Limitations (specific and honest):

• Coverage: Even with 100+ languages, many are still lightly represented; some language families appear only once, limiting cross-task conclusions.
• Audio conditions: Many datasets are clean or studio-quality; noisy, reverberant, compressed real-world conditions are under-tested.
• Model scope: 50+ models is strong, but the universe is bigger; some architectures and settings are missing.
• Length constraints: Most models are evaluated up to ~30s; long-form content (podcasts/lectures) isn’t fully captured.
• Missing capabilities: The benchmark doesn’t assess generation quality or real-time latency.

Required Resources:

• A single A100 GPU can run MAEB in a few to a couple dozen hours depending on model size (e.g., ~11–26 GPU-hours for some larger models vs. 23–345h in the extended set). Teams need storage for datasets, and familiarity with the MTEB interface.

When NOT to Use MAEB (or what to add):

• If your main goal is text-to-speech quality, speech synthesis naturalness, or music generation evaluation—MAEB does not measure these.
• If you must evaluate very long audio (hours-long recordings) end-to-end; length truncation may hide model strengths.
• If your use-case is ultra low-latency streaming; MAEB doesn’t benchmark real-time constraints yet.

Open Questions and Future Work:

• Can we design unified objectives that capture both acoustic (timbre, speaker) and linguistic (words, language ID) information without trade-offs?
• How do we expand robustly into multilingual cross-modal alignment so “English-only strength” transfers to 100+ languages?
• What losses or training signals explicitly improve clustering (e.g., neighborhood consistency, density-aware objectives)?
• How do we represent long-form structure (chapters, scenes) in embeddings that handle minutes or hours?
• Can we automate benchmark maintenance (dataset updates, bias checks) and add environmental footprints per run to guide greener research?

Takeaway of the Discussion:

• MAEB is a strong step toward universal audio representation testing, but it’s honest about gaps: multilingual, clustering, long-form, and real-world noise. By spotlighting the trade-offs and providing a living, open framework, it invites the community to improve together.

3-Sentence Summary:

• MAEB is a comprehensive, efficient benchmark that tests audio embeddings across 30 tasks, 7 categories, and 100+ languages, all integrated into the MTEB ecosystem.
• Experiments on 50+ models reveal no universal winner, large multilingual gaps, a persistent clustering weakness, and a trade-off between acoustic and linguistic representations.
• Strong MAEB embedding performance correlates with better Audio LLM reasoning, suggesting the benchmark is predictive of real-world multimodal ability.

Main Achievement:

• MAEB delivers a unified, scalable, and community-maintained evaluation framework that finally gives a trustworthy, big-picture view of audio embeddings across domains, tasks, and languages.

Future Directions:

• Build multilingual contrastive audio–text training at scale; explore architectures and objectives that jointly capture acoustic and linguistic cues; develop clustering-aware representation learning; extend to long-form and noisy conditions; and continue integrating with broader multimodal benchmarks.

Why Remember This:

• MAEB changes how we measure “good” in audio AI. Instead of single-skill bragging rights, it rewards balanced, real-world capability. It also sets a high bar for fairness (many languages), practicality (2–3× faster evaluation), and usefulness (predicting Audio LLM performance). If we want voice assistants, music search, wildlife monitoring, and safety systems to work for everyone, everywhere, MAEB shows the way.