Because the copilot App on Android today constantly fails while still charging my budget, I have to manually transfere a chat session to the webui. I now copy my questions and copilots answers, Q1, A1 and Q2,A2 to make copilot awate of the previous results ON MY BUDGET, which pisses me off
Q1: I am still concerned about efficiency?
What if people run the plugin with 96khz or even 192khz - is the transmittion volume then getting to big?
A1: This is a great efficiency concern. Let me work through the numbers concretely based on the code.
The Bandwidth Problem at High Sample Rates
Fixed chunk size is the core issue
BROADCAST_CHUNK_SAMPLES = 1470 is a fixed constant, and the comment says:
"Chosen so that one packet covers approximately 33 ms at 44.1 kHz."
The timer sends at 30 Hz, and every tick drains however many full 1470-sample chunks have accumulated. The packet count per second scales linearly with sample rate:
| Sample Rate |
Chunks/sec |
Bytes/packet* |
UDP bandwidth |
| 44,100 Hz |
~30/s |
~5,904 B |
~177 KB/s |
| 96,000 Hz |
~65/s |
~5,904 B |
~384 KB/s |
| 192,000 Hz |
~131/s |
~5,904 B |
~768 KB/s |
*Packet = 36 B header + 1470 × 4 B floats = 5,916 B
* This is per sender. With N instances, multiply by N.
The two real concerns
-
Loopback UDP volume — Since this is localhost-only, actual network bandwidth isn't the issue, but CPU overhead for the sender's sendto() + receiver's recvfrom() syscalls, memcpy, and mutex lock scales with packet rate. At 192 kHz you're doing ~131 syscalls/sec instead of ~30.
-
Ring buffer overflow — AudioSampleRingBuffer::CAPACITY = 16384 samples, sized for ~370 ms at 44.1 kHz. At 192 kHz, that same capacity only covers ~85 ms. If the 30 Hz timer fires late (which can happen), you'll start dropping samples (push() returns false on overflow).
-
Visual usefulness ceiling — The ScopeDisplay renders waveforms for a beat-range window. At 192 kHz you have 4× more samples describing the same musical time — far more resolution than any screen needs. The extra data doesn't add visual quality.
Potential mitigations
Option A: Downsampling before broadcast (most impactful)
Decimate the audio in processBlock before pushing to the ring buffer — e.g., only push every Nth sample where N = sampleRate / 44100. The waveform display doesn't need full-bandwidth audio.
Option B: Scale BROADCAST_CHUNK_SAMPLES with sample rate
Instead of a hardcoded 1470, compute the chunk size at prepareToPlay() to always represent a fixed time window (e.g. 33 ms), keeping packet rate constant at ~30/sec regardless of sample rate.
Option C: Increase ring buffer capacity at high sample rates
In prepareToPlay(), resize the ring buffer based on actual sampleRate to avoid overflow at 96/192 kHz.
Option A (decimation) is probably the cleanest fix — it solves bandwidth, CPU overhead, ring buffer pressure, and keeps the broadcast format completely unchanged. Would you like me to open a PR that implements one of these strategies?
Q2: But does decimate not completely change rms or canceling sensor to the worse?
A2: That's a great and sharp observation. You're right to be careful — naive decimation would hurt, but looking at the actual code, the situation is more nuanced. Let me break it down properly.
Your concern is valid — but only for one part
RMS: Decimation would NOT hurt it
RMS for remote instances is computed from the dedicated rmsAccum[] / rmsCount[] arrays accumulated in setRemoteRawData() (lines 91–94):
accum.rmsAccum[rmsSlot] += s * s;
accum.rmsCount[rmsSlot]++;
Then computeMetrics() reads it back as:
sqrt(ra[s] / (float)rc[s]) // = true RMS
This is a proper mean-square accumulation — it doesn't matter how many samples fall into a slot, the rmsCount normalises correctly. Decimating the stream (fewer samples arriving) would still yield correct RMS as long as the samples are representative, because more samples per slot just average more finely — the result converges to the same value.
Cancellation: This is where decimation gets risky
The cancellation index has two parts:
-
Denominator D: uses the same cancelAccum / cancelCount accumulator — this is RMS-based and robust to decimation.
-
Numerator N (line 376–389): computed from accum.bins[], which is a last-write-wins waveform buffer:
accum.bins[bin] = s; // last sample in this PPQ bin wins
The bins array is then used to compute the RMS of the summed waveform (local + remote) per cancel slot. This is a phase-sensitive operation — what matters here is that the bin contains the right sample at the right PPQ position for phase comparison with the local signal.
With heavy decimation, fewer samples per slot means fewer bin writes per beat cycle — the bins would be sparser but still written at valid PPQ-mapped positions. The phase accuracy depends on which sample wins for a given bin. This is already "one sample per bin" at native rate (last-write-wins), so sparse coverage doesn't fundamentally break phase alignment — it just reduces bin fill density, which could make the numerator N noisier.
The real answer: the better mitigation isn't decimation
Since the RMS is robust but the waveform bin fill density matters for cancellation quality, the cleanest fix is actually Option B from before — keep BROADCAST_CHUNK_SAMPLES time-constant:
// In prepareToPlay(), compute dynamically:
// chunkSamples = ~33ms × sampleRate
// = round(sampleRate * 0.033)
// At 44.1k → 1470, at 96k → 3168, at 192k → 6336
This keeps the packet rate fixed at ~30/s regardless of sample rate, packet size grows proportionally, but:
- ✅ RMS accuracy: unaffected (more samples = more precise accumulation)
- ✅ Cancellation bin density: stays the same or improves (more samples covering the same time window → denser bin fill)
- ✅ Packet rate / UDP syscalls: constant at ~30/s regardless of sample rate
- ⚠️ Packet payload size: grows 2× at 96 kHz, 4× at 192 kHz — but still only ~24 KB on localhost, totally fine
The ring buffer overflow risk (CAPACITY = 16384) also needs addressing alongside this — it should be sized to at least 2× the dynamic chunk size to avoid audio-thread drops at high sample rates.
Bottom line: Decimation would hurt cancellation metric accuracy (sparser bin coverage, potentially wrong phase wins), whereas a time-constant chunk size keeps everything correct and just allows naturally larger packets. Would you like me to open a PR for Option B?
Because the copilot App on Android today constantly fails while still charging my budget, I have to manually transfere a chat session to the webui. I now copy my questions and copilots answers, Q1, A1 and Q2,A2 to make copilot awate of the previous results ON MY BUDGET, which pisses me off
Q1: I am still concerned about efficiency?
What if people run the plugin with 96khz or even 192khz - is the transmittion volume then getting to big?
A1: This is a great efficiency concern. Let me work through the numbers concretely based on the code.
The Bandwidth Problem at High Sample Rates
Fixed chunk size is the core issue
BROADCAST_CHUNK_SAMPLES = 1470is a fixed constant, and the comment says:The timer sends at 30 Hz, and every tick drains however many full 1470-sample chunks have accumulated. The packet count per second scales linearly with sample rate:
*Packet = 36 B header + 1470 × 4 B floats = 5,916 B
The two real concerns
Loopback UDP volume — Since this is localhost-only, actual network bandwidth isn't the issue, but CPU overhead for the sender's
sendto()+ receiver'srecvfrom()syscalls, memcpy, and mutex lock scales with packet rate. At 192 kHz you're doing ~131 syscalls/sec instead of ~30.Ring buffer overflow —
AudioSampleRingBuffer::CAPACITY = 16384samples, sized for ~370 ms at 44.1 kHz. At 192 kHz, that same capacity only covers ~85 ms. If the 30 Hz timer fires late (which can happen), you'll start dropping samples (push()returns false on overflow).Visual usefulness ceiling — The
ScopeDisplayrenders waveforms for a beat-range window. At 192 kHz you have 4× more samples describing the same musical time — far more resolution than any screen needs. The extra data doesn't add visual quality.Potential mitigations
Option A: Downsampling before broadcast (most impactful)
Decimate the audio in
processBlockbefore pushing to the ring buffer — e.g., only push every Nth sample whereN = sampleRate / 44100. The waveform display doesn't need full-bandwidth audio.Option B: Scale
BROADCAST_CHUNK_SAMPLESwith sample rateInstead of a hardcoded 1470, compute the chunk size at
prepareToPlay()to always represent a fixed time window (e.g. 33 ms), keeping packet rate constant at ~30/sec regardless of sample rate.Option C: Increase ring buffer capacity at high sample rates
In
prepareToPlay(), resize the ring buffer based on actualsampleRateto avoid overflow at 96/192 kHz.Option A (decimation) is probably the cleanest fix — it solves bandwidth, CPU overhead, ring buffer pressure, and keeps the broadcast format completely unchanged. Would you like me to open a PR that implements one of these strategies?
Q2: But does decimate not completely change rms or canceling sensor to the worse?
A2: That's a great and sharp observation. You're right to be careful — naive decimation would hurt, but looking at the actual code, the situation is more nuanced. Let me break it down properly.
Your concern is valid — but only for one part
RMS: Decimation would NOT hurt it
RMS for remote instances is computed from the dedicated
rmsAccum[]/rmsCount[]arrays accumulated insetRemoteRawData()(lines 91–94):Then
computeMetrics()reads it back as:This is a proper mean-square accumulation — it doesn't matter how many samples fall into a slot, the
rmsCountnormalises correctly. Decimating the stream (fewer samples arriving) would still yield correct RMS as long as the samples are representative, because more samples per slot just average more finely — the result converges to the same value.Cancellation: This is where decimation gets risky
The cancellation index has two parts:
Denominator D: uses the same
cancelAccum / cancelCountaccumulator — this is RMS-based and robust to decimation.Numerator N (line 376–389): computed from
accum.bins[], which is a last-write-wins waveform buffer:accum.bins[bin] = s; // last sample in this PPQ bin winsThe bins array is then used to compute the RMS of the summed waveform
(local + remote)per cancel slot. This is a phase-sensitive operation — what matters here is that the bin contains the right sample at the right PPQ position for phase comparison with the local signal.With heavy decimation, fewer samples per slot means fewer bin writes per beat cycle — the bins would be sparser but still written at valid PPQ-mapped positions. The phase accuracy depends on which sample wins for a given bin. This is already "one sample per bin" at native rate (last-write-wins), so sparse coverage doesn't fundamentally break phase alignment — it just reduces bin fill density, which could make the numerator N noisier.
The real answer: the better mitigation isn't decimation
Since the RMS is robust but the waveform bin fill density matters for cancellation quality, the cleanest fix is actually Option B from before — keep
BROADCAST_CHUNK_SAMPLEStime-constant:This keeps the packet rate fixed at ~30/s regardless of sample rate, packet size grows proportionally, but:
The ring buffer overflow risk (
CAPACITY = 16384) also needs addressing alongside this — it should be sized to at least 2× the dynamic chunk size to avoid audio-thread drops at high sample rates.Bottom line: Decimation would hurt cancellation metric accuracy (sparser bin coverage, potentially wrong phase wins), whereas a time-constant chunk size keeps everything correct and just allows naturally larger packets. Would you like me to open a PR for Option B?