AI Listens for the Sounds of a Cow in Heat—and Slashes Data Storage 173-Fold

On a working dairy farm in Luoyang, in China’s Henan Province, more than a hundred Holstein cows now wear small neck-mounted acoustic tags that listen to every sound they make. Researchers report that by combining an enhanced audio-compression strategy with a team of machine-learning classifiers, these devices can pinpoint when a cow enters estrus—the brief window of fertility that dairy farmers must catch to keep their herds productive. The system, described in Smart Agricultural Technology, retained nearly all of the acoustically meaningful information in continuous barn recordings while cutting the stored data from 18 gigabytes to just 0.104 gigabytes, a reduction of roughly 173-fold.

Estrus detection is one of the most economically consequential tasks in modern dairy management. A cow that is not inseminated at the right moment misses a breeding cycle, extending the interval between calvings and reducing lifetime milk yield. Yet identifying the behavioral signs of heat in a barn holding hundreds of animals is notoriously difficult. Progesterone testing, often considered the gold standard, is costly and cannot provide continuous monitoring. Human observation, even when conducted at regular intervals, struggles against the sheer scale of a large herd and the irregular timing of behavioral onset.

Acoustic monitoring offers a tempting alternative because cows vocalize differently during estrus, and microphones can capture those changes without any invasive sampling. The challenge has always been practical: a continuous day of barn audio is dominated by machinery noise, fans, bird calls, and collisions of equipment, with only brief bursts of high-pitched cow calls embedded in the acoustic clutter. Storing and processing all of that raw audio for an entire herd quickly becomes an enormous data-management problem, and conventional lossy compression risks distorting the very timing and spectral cues that matter.

The research team, led by Jun Wang of Henan University of Science and Technology, built a custom dual-channel acoustic tag around an STM32 microcontroller. One unidirectional microphone, pressed against the cow’s neck, picks up vibrations from the animal’s own vocalizations, while a second omnidirectional microphone records airborne calls and ambient barn sound. An accelerometer tracks movement simultaneously. The waterproof device weighs 115 grams, runs for more than twelve hours per charge, and stores each audio channel on a separate memory card. Over two spring recording periods, the researchers equipped 116 Holstein cows with the tags and had experienced veterinarians score estrus behavior from ceiling-mounted surveillance video at three-hour intervals.

From 27 cows with complete 24-hour records spanning an entire estrus episode, the team catalogued vocalizations and split them by pitch. Low-pitched calls showed only a moderate association with estrus stage, with a Spearman rank correlation of 0.38, but high-pitched calls tracked the reproductive cycle far more closely, with a correlation of 0.74. During estrus, high-pitched vocalizations occurred in 99.3 percent of hourly intervals, and their mean duration more than doubled compared with the post-estrus period. Notably, first-time (primiparous) mothers were considerably more vocal than older cows, averaging about 161 seconds of high-pitched calling per hour of active vocalization versus 88 seconds for multiparous animals.

The heart of the method lies in what the authors call an enhanced SAX strategy—a reworking of the classic Symbolic Aggregate Approximation technique used in time-series mining. Each two-minute segment of audio, a window length chosen after testing alternatives and balancing correct recognition accuracy against both error rates and runtime, is transformed into a waveform image. The algorithm first normalizes the energy of every sample, then applies a gain compression whose strength varies sample by sample according to that energy. It then squeezes the waveform horizontally by dividing it into segments and keeping one representative value per segment. This dual vertical-and-horizontal compression produces 265 candidate images per window, and a selection rule based on structural similarity (SSIM) minus a penalty for average gain retains just one image per window.

The numbers behind this filtering are striking. Processing 46,080 minutes of raw audio generated more than six million temporary candidate images, from which the algorithm kept exactly 23,040 representatives. Crucially, this aggressive compression preserved the biologically relevant signal: 99.09 percent of the measured high-pitched vocalization duration survived, and a Jensen-Shannon divergence measure confirmed that the distributional change was far below the threshold that would indicate meaningful information loss. Cow calls were kept; machinery noise and birdsong were discarded.

Classification then proceeds on the retained images. A convolutional neural network with 12.8 million trainable parameters serves double duty, both assigning its own labels and providing a 512-dimensional feature embedding that feeds four additional classifiers: random forest, k-nearest neighbors, support vector machine, and XGBoost. A hard-voting scheme combines the five opinions into a single verdict for each image, and 30 consecutive image labels are aggregated into one cow-hour decision, with an hour classified as estrus when at least ten of the thirty images were flagged as high-pitched vocalization. On a pooled test set of 790 images, the ensemble correctly classified 782, achieving 98.99 percent accuracy with a 95 percent confidence interval of 98.01 to 99.49 percent, modestly outperforming every individual learner.

Timing matters as much as detection. In a same-farm comparison across 32 cows, the system’s predicted onset of estrus came before the veterinarian-reconstructed onset in 22 cases, with a mean prediction lead of roughly 89 minutes, and lagged behind in 10 cases by an average of about 28 minutes. Earlier alerts give farm staff a wider window to schedule insemination at the optimal moment, though the authors caution that the veterinary onset itself was reconstructed from three-hour observation intervals rather than minute-resolved measurements.

The researchers are careful to frame these results as an internal, single-farm evaluation. All animals were Holsteins managed under the same conditions, the image-level data split was not documented as cow-disjoint, and no external farm, breed, or climate was tested. The authors also note that dense or overlapping calls produced the highest error rates, that no measured edge-hardware benchmarks exist for the CNN, and that formal ablation studies are still needed to isolate the contribution of the gain term. Nevertheless, the framework demonstrates that a thoughtfully compressed acoustic representation can carry nearly all of the estrus-relevant information while making the data burden of continuous herd monitoring manageable, and it points toward acoustic estrus surveillance as a viable complement to accelerometers and hormonal testing in precision livestock farming.

Subject of Research: An acoustic estrus detection method for large-herd dairy cows combining enhanced SAX audio compression with heterogeneous ensemble learning

Article Title: Towards precise estrus identification of large-herd dairy cows: An acoustic detection method using enhanced SAX strategy and heterogeneous ensemble learning

Article References: Wang, J., Yue, Y., Wang, H., Aboelmaaty, A. M., Si, P., Zhao, K., Zhao, Y., & Shakweer, W. M. E.-S. (2026). Towards precise estrus identification of large-herd dairy cows: An acoustic detection method using enhanced SAX strategy and heterogeneous ensemble learning. Smart Agricultural Technology, 15, Article 102524. https://doi.org/10.1016/j.atech.2026.102524

Image Credits: AI Generated

DOI: 10.1016/j.atech.2026.102524

Keywords: dairy cows, estrus detection, acoustic monitoring, SAX compression, ensemble learning, precision livestock farming, bioacoustics, machine learning, Holstein cattle, reproductive management, wearable sensors, convolutional neural network