docs/baseline_experiments.md

Simple Spatial Token Baseline Experiments

This document defines two lightweight spatial-token baselines for comparing against the current SELD233-based spatial encoder in the Qwen spatial QA setup.

Goal

The current strong model path is:

FOA audio -> DCASE/SELD233 feature bridge -> SELD233 backbone -> spatial adapter
          -> 2.5 Hz spatial tokens -> projector -> Qwen LLM

For 20 s audio with task 235, the expected rate is:

feature frames: 100 Hz, hop_len=160
SELD frames: 10 Hz, feature_to_seld_ratio=10
spatial tokens: 2.5 Hz, downsample_factor=4
20 s segment: 50 spatial tokens

Every baseline below must match the same spatial token rate and prompt contract:

<|AUDIO|><|spatial|>

The comparison should answer whether the full SELD233 encoder is actually needed, or whether simpler spatial cues already explain most of the QA gains.

Baseline A: Raw FOA Intensity Vector Tokens

Use only the FOA intensity-vector channels as spatial input.

The existing feature bridge already computes baseline-compatible features:

[B, 7, T_feat, F]
channels 0..3: W/X/Y/Z log-mel
channels 4..6: FOA intensity-vector features

For task 235, F=128. The IV baseline should use channels 4..6 only.

Do not use full frequency averaging as the primary baseline. It is too lossy: it collapses [3, F] IV structure into only 3 numbers per frame before the 0.4 s temporal pooling step. Keep it only as a lower-bound sanity check.

Recommended default token construction:

IV features: [B, 3, T_feat, F]
frequency pooling: pool F into 8 or 16 mel bands -> [B, 3, T_feat, F_band]
temporal pooling: average every 40 feature frames -> [B, T_spat, 3 * F_band]
optional window statistics: concat mean/std/max over each 0.4 s window
token MLP: 3 * F_band * N_stats -> D_token, where D_token matches the current spatial token dim
projector: reuse the existing spatial projector into Qwen hidden size

For 20 s clips this gives:

T_feat=2000
T_spat=ceil(2000 / 40)=50

This is a valid low-capacity baseline. It gives the LLM explicit FOA direction cues without using the trained SELD233 temporal encoder.

Suggested IV variants:

• iv_3d: mean over all frequency bins, then temporal pool to 2.5 Hz. Use this only as a very weak lower bound.
• iv_band8: pool the 128 mel bins into 8 bands, then temporal pool to 2.5 Hz. This is the recommended first baseline.
• iv_band16: pool into 16 bands. Use this if iv_band8 is too weak.
• iv_band8_stats: pool into 8 bands and concatenate mean/std over the 0.4 s temporal window. Use this if source activity changes inside the window matter.

Important controls:

• Normalize IV features with the same frontend normalization when possible.
• Verify FOA axis convention before interpreting azimuth/elevation results.
• Keep the IV MLP small, otherwise it stops being a "simple feature" baseline.
• Report parameter count separately from the main SELD233 spatial branch.
• Report iv_3d separately from band-preserving IV. They answer different questions: iv_3d tests whether almost no spatial detail is enough; band IV tests whether simple spatial features are enough.

Baseline B: CNN Neural IV Tokens

Train a small CNN to convert audio-derived spatial features into token vectors.

Recommended input:

FOA feature tensor [B, 7, T_feat, F]
or IV-only tensor [B, 3, T_feat, F]

Recommended architecture:

2D CNN over time-frequency
temporal downsampling to 2.5 Hz
MLP projection to D_token
existing spatial projector to Qwen hidden size

The CNN should be deliberately small. A reasonable first version is:

Conv2d(C_in -> 32, kernel=3, padding=1)
GELU
Conv2d(32 -> 64, kernel=3, stride=(4, 2), padding=1)
GELU
frequency pooling
temporal pooling/resampling to T_spat
MLP(64 -> D_token)

This baseline answers a different question from raw IV:

Can a shallow trainable spatial frontend learn enough for QA without the
pretrained SELD233 backbone?

To avoid making this an unfair hidden strong encoder, keep the CNN shallow and train it only under the same QA supervision used by the other spatial QA runs.

Required Negative Controls

These controls are needed to interpret the baseline results.

• audio_only: base Qwen with no spatial tokens.
• zero_spatial: keep <|spatial|> placeholders but feed zero vectors.
• shuffled_spatial: feed valid spatial tokens from another sample in the batch or from another file with the same length.
• iv_shuffled: same as raw IV baseline, but shuffle IV tokens across samples.

If zero_spatial or shuffled_spatial performs close to the real spatial baseline, the model is mostly exploiting prompt/answer priors rather than the spatial tokens.

Optional Upper Bounds

These are not required for the first baseline pass, but they are useful if the results remain ambiguous.

• oracle_angle_bin: feed label-derived angle-bin IDs as tokens. This estimates whether the LLM can use perfect discrete spatial information.
• oracle_active_time: feed label-derived active-time bins. This estimates the ceiling for the simplified v4 time questions.
• direct_probe: train a small classifier on frozen spatial tokens for azimuth/elevation bin and active-at-time tasks. This separates encoder quality from LLM generation quality.

Training Matrix

Run all baselines on qa_pairs_v4 first. It avoids direct continuous angle and time regression, which was too brittle for causal LM text generation.

Recommended first-pass matrix:

audio_only
zero_spatial
shuffled_spatial
iv_tokens
iv_tokens + shuffled control
cnn_neural_iv
SELD233 stage3 model

Use the same QA data, LoRA target modules, learning rate range, and checkpoint selection rule where possible. For spatial-token baselines, start with:

train adapter/token frontend + projector + LLM LoRA
freeze Qwen base weights
freeze or omit SELD233 backbone

If the raw IV baseline is competitive with the full SELD233 branch, the current model may not be using the SELD233 temporal encoder effectively. If the CNN baseline is competitive but raw IV is not, then a lightweight trainable frontend may be enough for the QA task. If all simple baselines fail but SELD233 improves, the pretrained SELD encoder is adding useful structure.

Metrics

For qa_pairs_v4, report metrics by task and question class.

Core metrics:

azimuth bin choice accuracy
elevation bin choice accuracy
active-at-time yes/no accuracy
active-at-time balanced accuracy
time-bin choice accuracy
distance tolerance accuracy
count accuracy
source identification accuracy

Do not rely only on overall exact match. For yes/no and multiple-choice tasks, also compare against the class-prior baseline and random baseline:

yes/no random baseline: 50%
4-way choice random baseline: 25%
class-prior baseline: majority label accuracy in the test split

Implementation Notes

The clean implementation point is to add a pluggable spatial-token source before the existing projector path:

--spatial-token-source seld233 | iv | cnn_iv | zero | shuffled

All token sources should return:

spatial_tokens: [B, T_spat, D_token]
spatial_token_lengths: [B]

That keeps the existing tokenizer, placeholder expansion, RoPE modal order, and Qwen spatial injection path unchanged.

Precomputing token caches is acceptable and probably preferable for the first baseline pass:

precompute_iv_spatial_tokens.py
precompute_cnn_iv_spatial_tokens.py

The training script can then load these as spatial_tokens directly, bypassing the SELD233 backbone while preserving the same LLM-side path.

Main Risks

• Raw IV tokens may encode direction but not source identity. Source-specific questions still require the audio branch and LLM to select the right event.
• CNN Neural IV can become a strong encoder if it is too wide or too deep.
• Choice QA can overestimate spatial reasoning if distractors are weak. Use the v4 distractor design and keep the negative controls.
• Active-at-time QA must be balanced; otherwise a majority-class yes/no model can look good without using spatial or temporal evidence.