Embedded Alarm Sound Detection: Offline Alarm Recognition SDK with Graded ProtectionNEW
The Value of Alarm Detection: Scenarios and Workflow
A camera can see smoke, but it cannot hear a smoke alarm ringing while nobody is home. Alarm sound detection turns a passive recorder into an active guardian — but "an alarm" is not one sound. Smoke alarms, CO alarms and car alarms each carry a distinctive acoustic pattern, and recognizing which alarm matters far more than detecting "some alarm".
Scenario families: home safety cameras, elderly-care devices, in-vehicle and outdoor monitoring, and commercial security — all sharing the same engine.
The Signatures of Three Alarm Types
Below is a real alarm recording — waveform (top) and Mel spectrogram (bottom). The high-intensity sustained tone and regular harmonic stripes (the typical fingerprint of an alarm) are clearly visible:

Audio samples (real recordings — press play):
🔊 Alarm sound (real sample)
🔊 Another alarm recording
Smoke alarm — — a strong single-frequency pulse lasting several seconds; internationally standardized cadences (T3/T4) are common
CO alarm — — a regular pulse pattern (e.g., four pulses then a pause, the T4 pattern); the rhythm itself is the identity
Car alarm — — a sweeping "wail" or long tone with continuously gliding frequency — clearly different from fixed pulse patterns
These patterns are the key evidence: detection first verifies "this is an alarm-like pattern", then classifies which alarm it is.
How Recognition Works: Preprocessing → Features → Algorithms
The engine applies the standard pipeline (adaptive denoising, framing, time-frequency features) with pattern-centric decisions: single-frequency sustained tones, rhythmic pulse trains and frequency sweeps are three separable time-frequency trajectories. Relative-energy judgment keeps detection stable across quiet rooms and noisy kitchens. The main confusion sources — music, ringtones and speech — are handled through multi-class negative training.
Graded Notification: The Rules
1. Alarm event — pattern verification passed
2. Classification — smoke / CO / car / others
3. Graded notification — smoke or CO with nobody home means highest priority (push + phone call); car alarms are routine reminders
4. User handling — review, contact family, or mark as false alarm
Notification channels and grading rules are configurable per product and per household.
Elderly Care: When Hearing Is Protection
For seniors with reduced hearing or limited mobility, an alarm ringing unheard for minutes can escalate into an emergency. A care device that hears household alarms adds a layer cameras simply cannot cover.
Four categories matter most:
1. Smoke alarm — the most common trigger is an unattended stove; highest priority
2. CO alarm — invisible and odorless, higher risk when living alone; highest priority
3. Kettle / appliance whistles — catching a dry-boiling kettle early prevents escalation
4. Doorbell and phone rings — assistance when mobility is limited
The response chain: alarm detection → class and confidence → scene check (is the senior home?) → immediate notification (push + phone call to children) → escalation when unanswered (neighbors, property management, emergency contacts) → event record. Escalation timing and contacts are fully configurable.
Alarm occurrences cluster around cooking times — exactly the windows where care should be most attentive. Response metrics: push delivery within seconds, minutes-level average review, configurable escalation window.
Positioning statement: this is a care-assistance tool that speeds up human response — it does not replace emergency services.
Accuracy and Performance
In home scenes, smoke alarms dominate; detection stays above 93% even against kitchen noise and street backgrounds.
Note: performance figures are based on internal test environments; actual results depend on hardware and deployment scenarios.
Platform and Hardware Requirements
From 100 MHz-class chips upward; the model shares the SoC with the device's main workload; suitable for battery-powered care devices.
C API and Embedded Integration
The alarm sound detection library exposes a concise streaming C API: the caller just keeps feeding 16 kHz mono PCM; framing, Mel preprocessing and model inference run internally, and frame-level probabilities are aggregated by the alarm strategy into event callbacks.
Full interface declaration (alarm_detect.h):
/**
* alarm_detect.h — 警报声识别统一接口
*
* 封装 Mel 预处理 + 推理引擎 + 报警策略, 内部模型消费线程处理音频。
* 与录音模块 (audio_capture.h) 相互独立: 调用者自行决定音频来源
* (录音回调 / wav 文件 / 网络流), 通过 alarm_detect_feed 送入, 数据任意大小。
*
* 用法 (实时录音模式):
* alarm_detect_t *d = alarm_detect_create(mgk_path, NULL, NULL);
* alarm_detect_set_listener(d, on_frame, on_onset, on_offset, NULL);
* alarm_detect_start(d); // 启动内部模型消费线程
* audio_capture_start(rec, capture_cb, d); // 录音回调里调 alarm_detect_feed
* ...
* alarm_detect_stop(d); // 排空缓冲, 停止线程
* alarm_detect_destroy(d);
*
* 用法 (wav 文件模式):
* alarm_detect_t *d = alarm_detect_create(mgk_path, NULL, NULL);
* alarm_detect_set_listener(d, on_frame, on_onset, on_offset, NULL);
* alarm_detect_start(d);
* 循环读文件: alarm_detect_feed(d, pcm, n); // 任意数据大小
* alarm_detect_stop(d);
* alarm_detect_destroy(d);
*/
#ifndef ALARM_DETECT_H
#define ALARM_DETECT_H
#include <stdint.h>
#ifdef __cplusplus
extern "C" {
#endif
/* 识别事件 (报警策略输出, 用于事件结束回调) */
typedef struct {
float start_time; /* 事件开始时间 (秒) */
float end_time; /* 事件结束时间 (秒) */
float confidence; /* 事件置信度 */
float max_confidence; /* 事件内最大帧置信度 */
int frame_count; /* 事件持续帧数 */
} alarm_detect_event_t;
/* 帧级回调: 每帧识别结果 (模型线程内执行) */
typedef void (*alarm_detect_frame_cb_t)(float alarm_prob, float timestamp,
void *user_data);
/* 事件开始回调: 策略判定警报事件开始, 只有开始时间 */
typedef void (*alarm_detect_onset_cb_t)(float start_time, void *user_data);
/* 事件结束回调: 策略判定警报事件结束 (或停止识别时未结束的事件), 完整事件信息 */
typedef void (*alarm_detect_offset_cb_t)(const alarm_detect_event_t *event,
void *user_data);
typedef struct alarm_detect_s alarm_detect_t;
/* 创建/销毁; alarm_name/alarm_params 可传 NULL (用默认策略及参数) */
alarm_detect_t *alarm_detect_create(const char *mgk_path, /* 模型文件路径 (必填) */
const char *alarm_name, /* 报警策略名, NULL=默认 */
const char *alarm_params); /* 策略参数 key=val,key=val, NULL=默认 */
void alarm_detect_destroy(alarm_detect_t *det);
/* 设置事件回调 (create 后调用, 也可在运行中调整); 不需要的回调传 NULL */
void alarm_detect_set_listener(alarm_detect_t *det,
alarm_detect_frame_cb_t on_frame,
alarm_detect_onset_cb_t on_onset,
alarm_detect_offset_cb_t on_offset,
void *user_data);
/* 启动/停止识别: 启动内部模型消费线程 / 排空缓冲后停止线程 */
int alarm_detect_start(alarm_detect_t *det);
void alarm_detect_stop(alarm_detect_t *det);
int alarm_detect_is_running(alarm_detect_t *det);
/* 设置事件识别策略 (可在运行中调整) */
int alarm_detect_set_alarm(alarm_detect_t *det, const char *alarm_name,
const char *alarm_params);
/* 送入 PCM 数据 (16bit 单声道 16kHz), 线程安全, 任意数据大小 */
int alarm_detect_feed(alarm_detect_t *det, const int16_t *pcm, int num_samples);
#ifdef __cplusplus
}
#endif
#endif /* ALARM_DETECT_H */
A minimal WAV-inference demo (excerpt; the full file ships at src/alarmDetect/c/alarm_demo.c):
#include <stdio.h>
#include <stdlib.h>
#include <stdint.h>
#include "alarm_detect.h"
#define DEFAULT_MGK "alarm_detect_v7.mgk" /* 模型文件 */
#define DEFAULT_WAV "test_alarm.wav" /* 16kHz 单声道 16bit PCM */
#define FEED_CHUNK 16000 /* 每次送入 1 秒音频 */
/* 帧级回调: 每帧输出警报声概率 (约 1 秒一帧) */
static void on_frame(float alarm_prob, float timestamp, void *user_data)
{
(void)user_data;
printf("%8.2fs alarm=%.4f\n", timestamp, alarm_prob);
}
/* 事件开始回调: 策略判定警报事件开始 */
static void on_onset(float start_time, void *user_data)
{
(void)user_data;
printf("[EVENT] alarm start at %.2fs\n", start_time);
}
/* 事件结束回调: 应用层可据此做后续统计或分级响应 */
static void on_offset(const alarm_detect_event_t *ev, void *user_data)
{
(void)user_data;
printf("[EVENT] alarm end at %.2fs (dur=%.2fs, conf=%.3f, frames=%d)\n",
ev->end_time, ev->end_time - ev->start_time,
ev->confidence, ev->frame_count);
}
static int read_wav_pcm(const char *path, int16_t **pcm, int *n, int *sr); /* 完整实现见源文件 */
int main(void)
{
alarm_detect_t *det;
int16_t *pcm = NULL;
int num_samples = 0, sample_rate = 0;
int pos;
if (read_wav_pcm(DEFAULT_WAV, &pcm, &num_samples, &sample_rate) != 0)
return 1;
/* 1. 创建识别器: 模型文件 + 默认报警策略 (NULL) */
det = alarm_detect_create(DEFAULT_MGK, NULL, NULL);
if (!det) return 1;
/* 2. 注册回调 (均为可选) */
alarm_detect_set_listener(det, on_frame, on_onset, on_offset, NULL);
/* 3. 启动内部模型消费线程 */
alarm_detect_start(det);
/* 4. 分块送入 PCM; 实时录音时改在录音回调里 feed */
for (pos = 0; pos < num_samples; pos += FEED_CHUNK) {
int n = num_samples - pos;
if (n > FEED_CHUNK) n = FEED_CHUNK;
alarm_detect_feed(det, pcm + pos, n);
}
/* 5. 停止并销毁 */
alarm_detect_stop(det);
alarm_detect_destroy(det);
free(pcm);
return 0;
}
Build and run:
$(CC) alarm_demo.c -I. -L. -lalarmdetect -lpthread -lm -o alarm_demo
./alarm_demoConclusion
Understanding alarms means understanding which alarm — and what it should trigger. An online trial with full technical support is available.