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图片中是HIBY Project Ace的频响曲线，结合频响曲线的走向，与互联网风评，分析HIBY ...

Создано: 10 февраля 2026 г.

Отвечено с использованием GPT-5.2 Thinking от Chat01

Вопрос

image.pngimage.png

图片中是HIBY Project Ace的频响曲线，结合频响曲线的走向，与互联网风评，分析HIBY Project Ace的声音特点。

Python

python
from PIL import Image, ImageEnhance
img = Image.open('/mnt/data/image.png')
img.size


(1858, 946)

Python

python
import matplotlib.pyplot as plt, numpy as np
arr=np.array(img)
arr.shape, arr.mean()


((946, 1858, 3), 7.2501816801200984)

Python

python
import numpy as np
arr=np.array(img).astype(np.int16)
b=arr[:,:,2]; g=arr[:,:,1]; r=arr[:,:,0]
mask=(b>120) & (b-g>40) & (b-r>40)
mask.sum(), mask.shape


(16459, (946, 1858))

Python

python
# get coordinates of curve
ys, xs = np.where(mask)
xs.min(), xs.max(), ys.min(), ys.max()


(213, 1800, 116, 455)

Python

python
import matplotlib.pyplot as plt
plt.figure(figsize=(10,4))
plt.imshow(mask, cmap='gray')
plt.axis('off')
plt.show()

Python

python
gray = arr.mean(axis=2)
# detect grid lines and text: pixels brighter than 30
mask2 = gray>25
ys2, xs2 = np.where(mask2)
ys2.min(), ys2.max(), xs2.min(), xs2.max(), mask2.sum()


(23, 932, 53, 1827, 192256)

Python

python
row_counts = mask2.sum(axis=1)
col_counts = mask2.sum(axis=0)
# find y where row_counts>500 maybe
y_indices=np.where(row_counts>200)[0]
y_indices.min(), y_indices.max(), len(y_indices)


(35, 813, 130)

Python

python
x_indices=np.where(col_counts>200)[0]
x_indices.min(), x_indices.max(), len(x_indices)


(127, 1644, 128)

Python

python
x_indices=np.where(col_counts>80)[0]
x_indices.min(), x_indices.max(), len(x_indices)


(62, 1799, 371)

Python

python
x0_plot=200; x1_plot=1800
sub=mask2[:,x0_plot:x1_plot]
row_counts_sub=sub.sum(axis=1)
# find peaks rows with highest counts
peak_rows=row_counts_sub.argsort()[-20:][::-1]
peak_rows[:10], row_counts_sub[peak_rows[:10]]


(array([149, 147, 811, 148, 591, 259, 538, 539, 540,  36]),
 array([1590, 1590, 1590, 1590, 1589, 1589, 1589, 1589, 1589, 1589]))

Python

python
# pick rows with count>1500
rows=np.where(row_counts_sub>1500)[0]
rows[:20], rows[-20:], len(rows)


(array([ 35,  36,  91,  92,  93, 147, 148, 149, 202, 203, 204, 259, 260,
        261, 312, 313, 314, 368, 369, 370]),
 array([480, 481, 538, 539, 540, 591, 592, 593, 646, 647, 648, 701, 702,
        703, 759, 760, 761, 811, 812, 813]),
 46)

Python

python
rows = rows.tolist()
clusters=[]
cur=[rows[0]]
for r in rows[1:]:
    if r==cur[-1]+1:
        cur.append(r)
    else:
        clusters.append(cur)
        cur=[r]
clusters.append(cur)
centers=[int(np.mean(c)) for c in clusters]
centers, len(centers)


([35, 92, 148, 203, 260, 313, 369, 426, 479, 539, 592, 647, 702, 760, 812], 15)

Python

python
x0=213; x1=1800
# for each x in range, find y mean
curve_y=[]
curve_x=[]
for x in range(x0,x1+1):
    ys = np.where(mask[:,x])[0]
    if len(ys)>0:
        curve_x.append(x)
        curve_y.append(np.median(ys))
curve_x=np.array(curve_x); curve_y=np.array(curve_y)
# map to freq and dB
import numpy as np, math
logf0=math.log10(20); logf1=math.log10(20000)
freq=10**(logf0 + (curve_x-x0)/(x1-x0)*(logf1-logf0))
dB = 130 - (curve_y-35)/(812-35)*70
freq[:5], dB[:5], freq[-5:], dB[-5:]


(array([20.        , 20.08724399, 20.17486856, 20.26287537, 20.35126607]),
 array([119.5045045 , 119.45945946, 119.5045045 , 119.5045045 ,
        119.5045045 ]),
 array([19654.79683351, 19740.53498148, 19826.64713637, 19913.13492968,
        20000.        ]),
 array([97.11711712, 97.07207207, 97.02702703, 96.98198198, 97.02702703]))

Python

python
# get dB at ~1k
idx=np.argmin(np.abs(freq-1000))
freq[idx], dB[idx]


(1001.0664070174108, 115.45045045045045)

Python

python
points=[20,50,100,200,500,1000,2000,3000,5000,8000,10000,12000,15000,20000]
vals={}
for p in points:
    i=np.argmin(np.abs(freq-p))
    vals[p]=(freq[i], dB[i])
vals


{20: (20.000000000000004, 119.50450450450451),
 50: (50.106697562937406, 119.41441441441441),
 100: (100.10664070174107, 117.56756756756756),
 200: (200.00000000000003, 115.4054054054054),
 500: (501.06697562937416, 114.0990990990991),
 1000: (1001.0664070174108, 115.45045045045045),
 2000: (2000.0000000000005, 119.14414414414415),
 3000: (2998.0122172076926, 117.74774774774775),
 5000: (5010.669756293741, 121.48648648648648),
 8000: (7982.9647423395445, 117.11711711711712),
 10000: (10010.664070174107, 116.12612612612612),
 12000: (12018.713232041491, 110.45045045045045),
 15000: (15006.04659237203, 92.56756756756756),
 20000: (20000.000000000004, 97.02702702702703)}

Мысль

Formulating response based on sound signature and reputation

Zhihu isn’t working, maybe due to restriction or dynamic content. For reliable information, we can refer to Bilibili, Headfonia, MobileAudiophile, and Eddie’s blog. I’ll focus on crafting a Chinese response about the sound signature: neutral-bright, good vocals, tight bass, some sub-bass limitation, moderate mid-bass, and restrained upper mids with a 2k bump. I'll also discuss synergy suggestions like warm sources and foam tips. The internet reputation is mostly positive for clarity and vocals, but criticisms include sub-bass and treble sharpness.

Cross-referencing FR with reviews

The user wants a mix of frequency response (FR) and internet reviews. I'll point out that the 5-6kHz peak suggests clarity but might cause sharpness. The bass is flat with less sub-bass, though the FR shows it's higher than 1kHz—sub-bass isn't absent but lacks emphasis. I’ll also mention the earphone's dynamic driver for context, but clarify the FR is raw dB, with its relative shape being the focus.

Summarizing sound signature and review analysis

The FR doesn't fully reflect nuances like timbre. For clarity, I'll compare it against 500Hz or 1kHz. Given the user’s likely audiophile background, I'll describe the sound as leaning slightly bright and neutral with small vocal emphasis. The lack of boosted sub-bass and treble energy point to a well-balanced signature. I'll mention fitting genres (pop, acoustic, strings) and also suggest limitations for orchestral or EDM music.

I'll incorporate multiple reviews to back up claims, such as from Headfonia and MobileAudiophile.

Summarizing sound profile with sources and analysis

I'll compare the frequency response (FR) with online reviews: Headfonia discusses imaging and soundstage width, while Bilibili notes the bright yet transparent sound with low bass. Eddie's blog talks about balanced, warm sound with good vocal clarity. Using these sources, I'll structure the analysis in 4 parts: FR reading, cross-checking reviews, sound features, and genre pairings. I'll also include citation sources properly and mention the limitations of coupler differences.

Thought for 5m 57s

Ответ

下面按**“频响走向 → 听感推断”再用网上风评去交叉验证，给你一个相对稳的“声音画像”（说明：你这张图看起来更像未补偿/原始幅度**的频响，绝对 dB 值意义不大，主要看相对起伏与峰谷位置）。

1) 只看你这张频响：大方向是「中性偏亮、偏清晰的人声取向」

低频（20–200Hz）：不走“轰头/氛围”路线

  • 20–100Hz整体很平顺，没有明显的“低频大棚/低频隆起”。这类走向通常意味着：低频量感偏克制、重质感/线条，不追求大冲击。
  • 100–500Hz还有一点点缓慢下滑（低中频略收），往往带来：下盘不肥、整体更清爽，但厚度/肉感可能偏少。

这点和不少评测里提到的“低频更偏准确、冲击不夸张”一致：例如 Mobileaudiophile 说它的低频“tight and precise… favoring accuracy over impact”。 (Mobileaudiophile)
Headfonia 也直说“sub-bass notably lacking… mid-bass is decent”，并认为低频整体偏克制。 (Headfonia)

中频（200Hz–2k）：人声清晰，但可能“薄一点/不算浓郁”

  • 200–600Hz处于相对偏低的位置（低中频“地基”不算厚），容易带来：
    人声更干净、背景更黑，但男声胸腔/厚度可能不如暖厚系。
  • 1–2k有上扬，属于典型“把信息往人声清晰度/靠前感”推的做法：
    口型更明确、女声更容易亮/甜、咬字更清楚。

Headfonia评价它“very good vocal performance and a smooth mid-range”，但也提到整体偏线性、低频不足会让中频的“fullness”欠一点。 (Headfonia)
B站这篇（耳边评测）同样强调它“干净、通透、女声拿捏很准”，但提到男声有时会“少一点松弛、略紧”。 (Bilibili)

高频（3k–10k）：存在感强的亮点，也是争议点

  • 你图里最显眼的是约 5–6k 一段的明显峰值，这通常会带来：
    解析感、清晰度、镲片/拨弦的“亮度与边缘”更突出，听起来更“通透、亮丽”。
    但副作用是：对敏感的人可能出现偏锐/偏刺激/久听累。
  • 8–10k附近还有起伏，听感上更容易表现为：
    “亮、闪、空气感/金属泛音更明显”（同时也更吃录音与耳套/前端搭配）。

Mobileaudiophile明确点名“5.5–6k 的峰带来兴奋感，但有时会接近尖锐”，并建议用海绵套压一压亮度。 (Mobileaudiophile)
Headfonia也提到“lower treble… aggressive / boosted”，属于它的短板之一。 (Headfonia)
B站文里也把它的核心听感总结成“亮一点、更通透”，并说这种亮度把握得还算到位。 (Bilibili)

2) 结合“互联网风评”后的更完整画像

把多家评测/用户文的共识合起来，Project Ace（海神圈）的口碑大致是：

常被夸的点

  • 清晰、通透、信息量/解析强（尤其在人声与器乐分离上） (Bilibili)
  • 人声（特别女声）表现突出：位置比较合适，口型清楚、细节多 (Headfonia)
  • 声场偏“宽”+ 成像不错：宽度有惊喜，但纵深未必同级最强 (Headfonia)

常见争议/槽点

  • 低频党不一定满足：量感与“轰鸣/冲击”不是卖点，更多是收放与干净 (Headfonia)
  • 5–6k附近的亮/锐感：耐听度更吃人、吃套、吃前端；对高频敏感者可能疲劳 (Mobileaudiophile)
  • 有评测提到**部分乐器音色会略“怪/带点鼻音(honky)”**或整体“coherency”还能更好 (Headfonia)
  • 另外还有人提到佩戴人体工学问题（这会间接影响低频与整体平衡） (Headfonia)

3) 你可以把它理解成：

“偏线性中性、略偏亮的通透系单动圈；主打人声清晰与解析，低频不靠量取胜，高频有亮点也有风险。”

更适合的类型

  • 流行/女声/ACG、人声为主的录音、器乐小编制、弦乐与民乐（清晰、通透的优势能发挥出来） (Bilibili)

可能不那么对胃口的类型

  • 重低频氛围（EDM、嘻哈、需要下盘“推背感”的）
  • 大编制、强动态、信息密集且要厚度的（可能会觉得不够“磅礴/不够肉”） (Bilibili)

4) 搭配建议（跟风评高度一致）

  • 耳套：如果你觉得亮/刺激，优先试试海绵套或更“厚/暗一点”的硅胶套（很多人就是靠套子把 5–6k 的边缘感压下去）。 (Mobileaudiophile)
  • 前端：偏暖、偏顺滑的前端往往更稳；Headfonia也建议用暖声源增加“body/organic”，而 Eddie 的听感文里也描述了更强驱动下低频结构与整体饱满度会更好。 (Headfonia)

如果你告诉我你平时主要听的风格（比如“华语流行女声/ACG/摇滚/电音”）和常用前端（手机小尾巴？播放器？），我可以把这条频响再进一步落到更具体的“你大概率会觉得哪里爽、哪里不爽、怎么改最划算”。

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