Active acoustic inference

Scope

Sound is not a ready-made sequence of objects. A receiver obtains pressure over time through a medium, room, body, aperture, transducer, clock, calibration state, and preprocessing chain. Sources overlap; direct paths mix with delayed copies; motion changes the operator; an emitted probe changes both the evidence and the physical scene. The system must therefore infer sources and task state from a versioned, action-dependent measurement.

This chapter turns the acoustics, hearing, and auditory-scene-analysis audit into an engineering contract. The detailed equations live in operator-qualified acoustic inference, and Fixture F-009 tests the contract across nine hostile tracks. The editable systems diagram is kept in operator-qualified-active-acoustic-inference.mmd.

The chapter connects four existing parts of the project:

1. sensorimotor grounding, because head, body, receiver, and emitter actions change what can be known;
2. operator-qualified sensing, because acoustic inference is conditional on a physical forward operator;
3. sparse predictive compute, because multirate and event-driven representations can reduce traffic only under a complete task-and-energy test; and
4. the energy model, because sensing, emission, motion, communication, maintenance, and embodied hardware remain inside the service boundary.

The intended output is a calibrated decision, retained acoustic artifact, or safe action—not one universal “hearing” score. Detection, discrimination, intelligibility, grouping, separation, dereverberation, localization, ranging, uncertainty, and lifecycle efficiency remain distinct outcomes.

Biological observation

The ear is a level-dependent, multirate measurement system

Acoustic level has meaning only with a reference pressure, frequency and time weighting, integration interval, sensor position, calibration, and uncertainty (C-1054). Room quantities likewise depend on a declared procedure, frequency band, source-receiver geometry, and spatial and temporal support (C-1055). These constraints are part of the biological stimulus, not bookkeeping added after a model runs.

The cochlea implements frequency-dependent mechanical filtering whose response near characteristic frequency is compressively nonlinear. Outer-hair-cell motility contributes causally to that amplification, while psychophysical filter estimates depend on centre frequency and fitting assumptions (C-1056–C-1058). Temporal coding also has regimes: envelope sensitivity is band-limited, auditory-nerve phase locking degrades with frequency, and downstream circuitry can sharpen synchrony in scoped low-frequency conditions (C-1059–C-1061).

“Cochlear,” “spiking,” and “neuromorphic” are therefore insufficient mechanism descriptions. A transferable front end must state its filter shapes, bandwidths, sample rates, level dependence, compression history, saturation, thresholds, refractory periods, timestamp precision, and calibration state. FFT, wavelet, mel, ERB, gammatone, gammachirp, modulation, and learned filters remain competing implementations; cochlear-like filtering is already conventional signal processing (C-1095).

Spatial hearing combines bounded cues with movement

Coincidence-sensitive circuits can encode interaural time differences, but the useful cue changes with frequency content, head and pinna transfer functions, room response, and source spectrum. Interaural level differences and spectral cues contribute on different bands and geometries (C-1062–C-1063). Head motion can disambiguate otherwise similar observations (C-1064); it is an information-acquisition action, not nuisance augmentation.

Early-arriving spatial information can dominate later copies, while spatial separation can improve recognition under masking (C-1065–C-1066). That does not make reverberation irrelevant. Stable spectrotemporal context and prior room exposure can alter later judgments; monaural context can compensate for some reverberant speech distortion; direct-to-reverberant energy contributes to distance judgments (C-1070–C-1073). The useful state therefore includes direct, early, and late components plus their time-varying relation to source and receiver motion.

Auditory objects are inferred under masking

Energetic masking concerns overlap at the receiver. Informational masking also depends on whether target and masker are perceptually segregated, and selective listening changes strongly with ear and source configuration (C-1067–C-1068). Temporal coherence across frequency channels is a plausible grouping mechanism, not a settled universal decomposition rule (C-1069).

This makes four outcomes non-interchangeable:

• detection: whether task evidence is present;
• grouping: which events belong together and how uncertain that assignment is;
• separation: recovery of one or more waveforms under an explicit reference and permutation contract; and
• causal identity: which physical source produced a recovered component.

A separator can improve a waveform metric while switching identities, damaging localization, or changing perceptually important structure. Published separation metrics can hide severe waveform changes (C-1094), so no one metric is allowed to stand in for the scene.

Sparse activity and efferent control have narrow causal interpretations

Awake auditory cortex can represent sounds with low population activity, and sparse-coding objectives can learn cochlea-like kernels in some sound ensembles. The learned code depends on the training ensemble (C-1074–C-1076). Sparse activity is evidence about representation, not a direct measurement of system joules. Sensors, clocks, memory traffic, event routing, decoding, idle power, and training may dominate (C-1097).

Efferent activation can causally reduce cochlear gain and improve masked-tone auditory-nerve responses in the studied preparations (C-1077). A general human speech-in-noise benefit from medial olivocochlear strength remains disputed (C-1078). An AI analogue earns the name only when an intervention on task-conditioned front-end gain changes a registered external endpoint after distortion, recovery time, calibration, and energy are charged. An attention map or smaller hidden activation is not that evidence.

Echolocation is active observation with physical consequences

Echolocators vary call timing with spatial task difficulty, adapt emission level with distance, steer or reshape the acoustic field of view, and coordinate head or pinna motion with emission (C-1079–C-1082). Human experts can use self-generated echoes, but the demonstrated capability includes extensive experience and neural reorganization (C-1083). Spectral jamming avoidance in bats is context-dependent rather than universal (C-1084).

The transferable loop is emit, receive, infer, move or re-aim, and decide. Its benefit is conditional on waveform ambiguity, target reflectivity, range, clutter, association, other emitters, detectability, acoustic exposure, and the energy required to produce and receive the probe (C-1085, C-1096).

Proposed AI translation

Preserve the episode, operator, and action identity

For episode $e$, retain the contract

where:

Calibration identity can become part of the learned task when a model depends on device, room, or preprocessing state (C-1098). Train-test splits must group clean sources, convolved variants, neighboring windows, impulse-response and renderer lineages, HRTF or array families, devices, speakers or listeners, and derived labels. Capture time and receipt time remain distinct, and only causally received observations may affect a decision.

Keep pressure, level, exposure, and energy separate

For pressure $p(t)$ in pascals over interval $T$ in seconds,

where $p_{\mathrm{rms}}$ is RMS pressure [Pa], reference pressure $p_0=20\,\mu\mathrm{Pa}$ in air, and $L_p$ is sound-pressure level [dB re $20\,\mu\mathrm{Pa}$]. Frequency weighting, time weighting, band, position, orientation, and calibration must be explicit. Sound exposure $\int p^2(t)dt$ has units $\mathrm{Pa^2\,s}$; it is not acoustic emission energy [J], electrical energy [J], or a complete biological risk model.

For source pressure $x_s(t)$, received pressure $y_m(t)$, and noise $n_m(t)$, all in pascals, use the time-varying mixture

where $m$ is receiver-channel index [1], $s$ is source index [1], source count $N_s$ is dimensionless, $t$ and delay $\tau$ are in seconds, and propagation kernel $h_{m,s}(t,\tau)$ is in reciprocal seconds. A fixed convolution is only a special case; moving sources, receivers, boundaries, media, and emitters make the operator time-varying.

Build a qualified multirate front end

The front end may combine:

1. an FFT, wavelet, constant-Q, mel, ERB, gammatone, gammachirp, modulation, or learned analysis bank;
2. level- and history-dependent compression with declared attack, release, saturation, and distortion;
3. masking and source-count hypotheses rather than a single global noise scalar;
4. dense samples or sparse onset, offset, change, phase, or threshold events with timestamp and refractory identity; and
5. a decoder or downstream task that is charged for reconstructing information discarded by the encoder.

Band selection and update rate should follow the causal time scales needed by the task. Fine timing cannot be assumed above the supported band; weak sustained signals and rare hazards cannot be discarded merely because an event count falls.

Fuse spatial evidence without erasing ambiguity

For path-length difference $\Delta r$ [m] and sound speed $c_{\mathrm{snd}}$ [m/s], interchannel delay is

where $\Delta t$ is in seconds. For left and right RMS pressures $p_L,p_R$ [Pa],

is interaural level difference [dB]. Both cues are band-, source-, room-, body-, and calibration-qualified. Spectral cues and head or receiver motion remain separate intervention axes. A correlation surface under reverberation may be multimodal; reducing it to one delay without calibrated uncertainty loses the ambiguity the controller needs.

The learned path must be compared with generalized cross-correlation, matched filtering, steered-response power, delay-and-sum, robust and minimum-variance beamforming, MUSIC or ESPRIT, Bayesian delay estimation, and source tracking. GCC, MVDR, and subspace localization are mature nulls, not discoveries created by a biological label (C-1086–C-1088).

Separate scene inference from metric substitution

Each output head declares one literal construct and uncertainty model. The minimum firewall is:

Independent-component methods, time-frequency sparsity methods, deep clustering, permutation-invariant training, and time-domain separation are mature but assumption-bound baselines (C-1089–C-1093). The strongest compatible composition—not an isolated weak separator—is the decisive null.

Close the action loop

At decision time $t$, policy $\pi_q$ for method $q$ selects

where $\mathcal H_t$ is causally received history, $\widehat X_t$ is estimated scene state, $\widehat O_t$ is operator and calibration state, $\widehat U_t$ is uncertainty, $\mathcal A_t^{\mathrm{safe}}$ is the feasible action set, and $B_t$ is remaining componentwise budget. Emission actions set waveform, spectrum [Hz], level [referenced dB], aim [degree or rad], and time [s]; body actions set pose and motion [m, rad, s]; sensor actions select aperture [m], channels, and sample rate [sample/s]; gain is dimensionless or declared in dB; the task action uses its native unit.

For monostatic echo emission and receipt times $t_{\mathrm{emit}}$ and $t_{\mathrm{recv}}$ [s], nominal range is

where $\widehat r$ is range [m]. The interval must propagate clock error, target motion, refraction, multipath, ringing, waveform ambiguity, association, and detector threshold. Multiple emitters add collision, authentication, fairness, adversarial imitation, and jamming state. The proposal must beat TDMA, FDMA, CDMA, random access, listen-before-talk, power control, coded probes, cancellation, authentication, and game-theoretic allocation at equal spectrum, power, messages, and exposure.

One system, tested against a complete null

flowchart TB
    scene["Sources · interferers · targets<br/>geometry · medium · room · motion"] --> field["Time-varying acoustic field<br/>direct path · reflections · masking · clutter"]
    emit["Active emission<br/>waveform · level · spectrum · aim · timing"] --> field
    body["Receiver/body/array action<br/>pose · head motion · aperture · gain"] --> operator["Versioned observation operator<br/>transducer · HRTF/array · clock · calibration · support"]
    field --> operator
    operator --> received["Causally received waveform/events<br/>pressure · timestamps · missingness · uncertainty"]
    received --> front["Front end<br/>filterbank · compression · sparse events"]
    front --> infer["Infer and calibrate<br/>detect · group · separate · localize · range"]
    infer --> decide["Act · emit · move · query · abstain"]
    decide --> emit
    decide --> body
    infer --> firewall["Outcome firewall<br/>detection · intelligibility · grouping · separation<br/>direction · range · calibration · risk"]
    nulls["Mature null stack<br/>ERB/FFT/AGC · GCC/MVDR/MUSIC<br/>ICA/DUET/WPE/PIT/TasNet<br/>Bayes/POMDP/MPC/VOI · spectrum protocols"] --> compare{"Equal operator/action/lifecycle budget"}
    firewall --> compare
    ledger["Complete ledger<br/>samples · bytes · seconds · person-hours<br/>emission/exposure · operational + embodied joules"] --> compare
    compare --> retain["Retain literal track residual"]
    compare --> retire["Retire composition<br/>preserve acoustic contract"]

Editable source: operator-qualified-active-acoustic-inference.mmd.

The contract is owned by existing components:

• Candidate 002 — Multiscale context broadcast for multirate context;
• Candidate 006 — Reversible physical skill for reusable analog or beamforming paths;
• Candidate 007 — Endogenous observation surveillance for action-dependent observation;
• Candidate 009 — Graded assurance envelopes for calibration, uncertainty, exposure, and fallback;
• Candidate 012 — Latency-qualified authority for travel time, evidence age, and decision authority; and
• Candidate 014 — Versioned observation contract for operator and calibration identity.

Efficiency mechanism

The proposed composition can improve efficiency through four literal pathways:

1. Multirate analysis. Slow envelopes and long context need not update at the rate required for fine timing. Savings must appear in samples, memory traffic, latency, and measured joules without degrading a protected band.
2. Conditional events. Onsets, offsets, changes, or uncertainty triggers can suppress redundant transport and downstream work. The decoder, timestamping, missed sustained signals, false events, and idle event hardware remain in the comparison.
3. Selective physical action. A head turn, receiver move, gain change, or acoustic probe can resolve an ambiguity more cheaply than processing another passive interval. Its motion, emission, exposure, delay, detectability, and interference costs must be lower than the decision value it creates.
4. Reusable calibrated structure. Stable filter, beamforming, correlation, or control paths can move to DSP, FPGA, ASIC, analog, or neuromorphic hardware when reuse amortizes conversion, characterization, drift monitoring, repair, and replacement.

For one registered service interval, lifecycle energy is

where method $q$ has data-acquisition, training, acoustic-emission, sensing, inference, motion, communication, storage, calibration, maintenance, and amortized embodied-energy terms, each measured in joules over the same service interval. The complete ledger also reports:

• pressure samples, sparse events, optimization steps, queries, and bytes;
• wall time [s], task latency [s], peak memory [byte], and network traffic [byte];
• acoustic exposure [$\mathrm{Pa^2\,s}$], unsafe-event count [1], and harm or detectability in a registered task-specific unit;
• emitted acoustic energy [J], electrical input [J], sensing [J], motion [J], compute [J], cooling [J], and facility allocation [J];
• recording, labeling, listening tests, calibration, tuning, monitoring, maintenance, and repair [person-hour]; and
• transducers, arrays, processors, actuators, replacements, wear, embodied hardware, and opportunity cost.

An arm that exceeds any binding budget is infeasible. Resources released by an ablation remain unused. Lower event count, lower FLOPs, quieter emission, or a faster accelerator is not an efficiency result unless accepted task service improves on the complete protected vector.

Evidence status

The stable claim ledger mirrors all 46 audit-local claims without promoting the audit itself into evidence:

No row establishes that the complete proposed system beats the conventional stack. The evidence supports the physical constraints, biological phenomena, and engineering nulls. The composition remains an experiment.

Speculative extensions

Operator-conditioned auditory state

Learn a compact state that jointly represents room response, body or array transfer, clock uncertainty, source hypotheses, and front-end state. It must predict held-out calibration probes and remain identifiable under scene changes. Compare it with explicit system identification, adaptive filtering, room banks, and Bayesian state-space models.

Counterfactual acoustic action

Train the policy to predict not only what a probe or head turn might reveal but also its exposure, detectability, collision, motion, and lifecycle cost. Use paired simulator seeds and physical replay so action value is estimated against the same latent scene, not easier episodes selected after acting.

Cross-timescale front-end control

Let fast protection and compression, intermediate event routing, and slower room or task adaptation share state without sharing unrestricted authority. Test whether the joint controller beats independently tuned AGC, PCEN, adaptive filters, selective prediction, and recurrent context after coordination cost.

Population-qualified acoustic hardware

Route recurring transforms among digital, analog, array, and event-driven front ends by measured device identity, calibration envelope, workload reuse, and remaining service life. Hold out hardware classes, lots, transducers, and array geometries. A nominal-device gain does not establish fleet-level benefit.

Strategic multi-emitter sensing

Model emitters as cooperative, selfish, deceptive, imitating, colluding, or turnover-prone agents. Joint waveform and scheduling policies must retain source authentication and calibrated uncertainty under never-coordinated population cross-play. Ordinary spectrum protocols remain the null.

Failure modes

Measurable predictions

The nine F-009 tracks convert the chapter into falsifiable predictions:

1. Level-dependent front end. A qualified nonlinear multirate front end improves registered detection or discrimination across held-out levels, bands, histories, sensors, and domains beyond the strongest fixed and learned filterbank-plus-compression stack at equal bytes, latency, and joules.
2. Sparse timing. Event encoding preserves protected timing, weak sustained sources, and rare hazards while reducing measured memory traffic and service energy beyond VAD, codecs, sparse convolution, and duty-cycled DSP.
3. Active emission and reception. Joint waveform, emission, and receiver action creates positive causal decision value after exposure, detectability, motion, interference, calibration, and lifecycle energy are charged.
4. Localization under operator shift. Calibrated direction and range remain better than GCC, Bayesian delay, SRP, MVDR, MUSIC, and tracking baselines on held-out HRTFs, arrays, bodies, apertures, rooms, spectra, faults, and clocks.
5. Reverberation adaptation. Context state improves literal waveform, intelligibility, event identity, distance, or calibration outcomes in new rooms beyond WPE, Wiener filtering, explicit operator estimation, and equal-sample adaptation without confusing compensation with recovery.
6. Grouping and separation. The model improves event-to-source grouping, source count, separation, causal identity, localization, uncertainty, and downstream value across new sources, rooms, counts, and cue conflicts beyond the complete ICA, IVA, DUET, NMF, WPE, PIT, deep-clustering, TasNet, and neural-beamforming stack.
7. Efferent-like gain. Randomized intervention on task-conditioned gain improves a protected external endpoint after distortion, false suppression, recovery, calibration, and energy are included; internal suppression alone predicts no pass.
8. Multi-emitter interference. Adaptive emission coordination improves ranging or detection, association, throughput, fairness, calibration, and exploitability under unseen cooperative and adversarial populations beyond mature spectrum-access and authentication protocols at equal power and messages.
9. Complete lifecycle. Any surviving benefit replicates across at least two source families, rooms or media, arrays or bodies, operator versions, scene types, model families, sites, and hardware classes after every acquisition, action, person-hour, risk, operational-energy, and embodied-energy term is charged.

Preregister protected outcomes, non-inferiority margins, material-improvement thresholds, 95% intervals, multiplicity control, tail-failure ceilings, and stopping rules. Randomize and analyze at the unit receiving the intervention; adjacent samples and overlapping windows are not independent sources.

The hard decision is simple: if the complete conventional composition matches the proposal at equal operator, action, calibration, uncertainty, exposure, human-work, and lifecycle budgets—or if no ablation isolates value beyond that stack—retire the architectural residual. Keep the measurement, operator, action, metric, and accounting contract.